Noise Maps for Quantitative and Clinical Severity Towards Long-Term ECG Monitoring

Transcription

1 enor Article Noie Map for Quantitative and Clinical Severity Toward Long-Term ECG Monitoring Etrella Ever-Villalba ID, Francico Manuel Melgarejo-Meeguer, Manuel Blanco-Velaco ID, Francico Javier Gimeno-Blane 3, Salvador Sala-Pla 4, Joé Lui Rojo-Álvarez 5,6 ID and Arcadi García-Alberola, * Cardiology Service, Arrhythmia Unit, Hopital General Univeritario Virgen de la Arrixaca, El Palmar, Murcia 3, Spain; etrella.ever@urjc.e (E.E.-V.); francico.melgarejo@goumh.umh.e (F.M.M.-M.) Department of Signal Theory and Communication, Univerity of de Alcalá, Alcalá de Henare, Madrid 885, Spain; manuel.blanco@uah.e 3 Department of Signal Theory and Communication, Miguel Hernández Univerity, Elche, Alicante 3, Spain; javier.gimeno@umh.e 4 Intituto de Neurociencia, Miguel Hernández Univerity CSIC, Alicante 355, Spain; alvador.ala@umh.e 5 Department of Signal Theory and Communication, Rey Juan Carlo Univerity, Fuenlabrada, Madrid 8943, Spain; joelui.rojo@urjc.e 6 Center for Computational Simulation, Univeridad Politécnica de Madrid, Boadilla, Madrid 83, Spain * Correpondence: arcadi@ecardiologia.e; Tel.: Received: 7 September 7; Accepted: October 7; Publihed: 5 October 7 Abtract: Noie and artifact are inherent contaminating component and are particularly preent in Holter electrocardiogram (ECG) monitoring. The preence of noie i even more ignificant in long-term monitoring (LTM) recording, a thee are collected for everal day in patient following their daily activitie; hence, trong artifact component can temporarily impair the clinical meaurement from the LTM recording. Traditionally, the noie preence ha been dealt with a a problem of non-deirable component removal by mean of everal quantitative ignal metric uch a the ignal-to-noie ratio (SNR), but current ytem do not provide any information about the true impact of noie on the ECG clinical evaluation. A a firt tep toward an alternative to claical approache, thi work aee the ECG quality under the aumption that an ECG ha good quality when it i clinically interpretable. Therefore, our hypothee are that it i poible (a) to create a clinical everity core for the effect of the noie on the ECG, (b) to characterize it conitency in term of it temporal and tatitical ditribution, and (c) to ue it for ignal quality evaluation in LTM cenario. For thi purpoe, a databae of external event recorder (EER) ignal i aembled and labeled from a clinical point of view for it ue a the gold tandard of noie everity categorization. Thee device are aumed to capture thoe ignal egment more prone to be corrupted with noie during long-term period. Then, the ECG noie i characterized through the comparion of thee clinical everity criteria with conventional quantitative metric taken from traditional noie-removal approache, and noie map are propoed a a novel repreentation tool to achieve thi comparion. Our reult howed that neither of the benchmarked quantitative noie meaurement criteria repreent an accurate enough etimation of the clinical everity of the noie. A cae tudy of long-term ECG i reported, howing the tatitical and temporal correpondence and propertie with repect to EER ignal ued to create the gold tandard for clinical noie. The propoed noie map, together with the tatitical conitency of the characterization of the noie clinical everity, pave the way toward forthcoming ytem providing u with noie map of the noie clinical everity, allowing the uer to proce different ECG egment with different technique and in term of different meaured clinical parameter. Senor 7, 7, 448; doi:.339/7448

2 Senor 7, 7, 448 of 3 Keyword: noie map; ECG; noie clinical everity; Holter; external event recorder; long-term monitoring; noie bar. Introduction The electrocardiogram (ECG) ignal, which depict the electrical activity of the heart, i an efficient tool to oberve it health condition becaue it depict ueful electrical information on the cardiac ytem collected by noninvaive mean uing a et of electrode attached to the urface of the human body []. Wherea the uual ECG regiter the ignal during hort period of a few econd, Holter monitoring allow u to record the ECG for longer time interval, typically of 4 h. Nowaday, it ha become uual to ue thi in order to find anomalie that do not appear in hort-term ECG, uch a paroxymal, elf-limited arrhythmia preenting a epiode eparated by hour or day []. It i alo a very attractive method to provide health care olution in combination with the information and communication technologie [3,4], including health monitoring in remote area uing mobile device [5,6]. Currently, the interet in monitoring patient over everal day i growing becaue of it potential to find anomalie that remain undetected in tandard ECG and 4 h Holter. Thi kind of regiter i referred to a long-term monitoring (LTM) recording, and thee have been found to be ueful for the detection of ubclinical atrial fibrillation in patient with cryptogenic troke [7,8], for the detection of non-utained atrial or ventricular arrhythmia in patient with heart failure [9], and for the aement of autonomic parameter obtained from heart rate variability analyi []. Holter ECG i obtained from portable device while patient continue with their daily activitie; thereafter, the ignal i highly affected by noie and artifact. Thu, a a reult of the extenive ue of Holter ECG, noie ha become a matter of major concern, particularly in LTM recording, becaue it preence may reult in wrong diagnoe. The main ource of noie are patient movement (baeline wander noie, electromyographic noie, and electrode motion), powerline or electronic-device interference at data collection, ignal proceing, or medical equipment []. Traditionally, dealing with poor-quality ECG ha been faced a a denoiing problem for which the target conit of improving ome quality metric, uch a the root mean quare (RMS) or the ignal-to-noie ratio (SNR), which are often meaured on artificially contaminated ECG. Recent quantitative analyi ha been performed within thi framework uing different ignal proceing technique, uch a tranform domain [ 6], independent and principal component analyi [7,8], adaptive filtering [9], genetic algorithm [], empirical mode decompoition [,], fuzzy logic [3], or neural network [4 6]. The underlying aumption of thee approache i that the denoiing proce can provide a valid ignal if the reulting metric value are olid enough. However, none of the ued meaurement parameter report information about diagnotic feature allowing u to ae their clinical validity. For example, we note that ometime, a moderate SNR doe not affect the ECG morphology, wherea imilar SNR in different condition may dramatically decreae the ignal quality. Additionally, on ome occaion, the ECG i o trongly altered that none of it wave are recognizable; thu they become uele. In conequence, rather than noie elimination, an alternative approach i followed in recent reearch focuing on two apect, namely, noie quantification from a clinical tandpoint, and ECG quality aement. The quality i conidered in thi work a a ynonym of clinical validity; that i, a ignal i conidered good quality when it can be ued for clinical purpoe. According to thi view, the ignal proceing of LTM for quality ECG purpoe would no longer be limited to artifact removal, but it hould alo include the identification of everely corrupted egment, which are clinically invalid with different everity level, in order to avoid fale diagnoi. One of the mot clarifying contribution following thi approach i given in [7], which propoe a method for claifying the quality of an ECG into five level. The paper i alo a good ummary of previou work on thi topic. Other example of

3 Senor 7, 7, of 3 automatic detection and claification of noie are [8,9], although thee two work do not rate the noie in the ECG from a clinical tandpoint. Therefore, and according to thi novel approach, our hypothei i that it i poible (a) to create a clinical everity core for the effect of noie on the ECG, (b) to characterize it conitency in term of it temporal and tatitical ditribution, and (c) to ue it for LTM evaluation of ignal quality, providing a better criterion for diagnotic index extraction when compared to conventional quantitative noie magnitude meaurement. To tet thi hypothei, we crutinized the following element. Firt, we propoed to create and ue noie map, defined a a imple overview of the temporal ditribution of the proportion of noie with different quality throughout time window with adequate duration for long recording. Second, we propoe to create a meaurement of the noie clinical everity, according to the criteria of a pecialit on the impact that different noie intenitie and preence will have on ubequently meaured clinical indice. Thi noie clinical everity will be potentially ued a the gold tandard for analyzing the conventional quantitative noie meaurement. We ubequently analyzed the noie clinical everity meaurement for long-term recording from ECG external event recorder (EER) in patient and in one cae from 7 day Holter monitoring on a detailed timeline. A preliminary verion of thi work ha been previouly introduced [3]. The cheme of the paper i a follow. In Section, we decribe our patient databae and the LTM Holter patient cae. The clinical everity criteria for ECG noie are alo decribed, a well a the tate-of-the-art proxie for quantitative noie and the definition of noie map. The reult are preented, and the achieved noie claification criteria are ummarized in Section 3, together with the noie map for clinical and quantitative noie criteria; the noie tatitical ditribution are crutinized both in the EER databae and in the LTM Holter cae example. Some relevant conideration for the powerline noie and the inter-oberver variability are alo included therein. The dicuion and concluion of the preent work are finally addreed in Section 4.. Material and Method Thi ection i tructured a follow. Firtly, the recording ued to addre the LTM analyi are preented in two cenario. Secondly, the methodology followed for launching a clinical everity gold tandard i ummarized, and everal quantitative metric of noie are introduced. For all of thee kind of noie, their characterization i then addreed in term of their time ditribution, by defining the noie map, and in term of their amplitude ditribution, by uing their by-ample tatitical ditribution of the etimated noie... Material The data ued for thi tudy were gathered from two different continuou-recording ECG ource, namely, an EER (Sorin SpiderFlah-t) and a 7 day Holter device (Delmar Reynold Lifecard CF). Data were obtained from clinical indication from the Arrhythmia Department of the Univerity Hopital Virgen de la Arrixaca at Murcia, Spain. EER device perform continuou ECG ambulatory monitoring, and they alo analyze the ignal in real time, looking for QRS complexe (correponding to ventricular depolarization deflection). Arrhythmic event detection i baed on the rate and regularity of the QRS erie [3], but in practice, noie and artifact alo often trigger the event recording, even during inu rhythm. Every detected event (arrhythmia, artifact, or noie) trigger an automatic recording with a duration of about 3 to 3, at a ampling frequency of Hz. The ytem ha three electrode, yielding two ignal for ubequent analyi. Our databae conited of data from patient (5 women and 5 men, 65. ± 3.5 year) who had been referred to the hopital for palpitation, yncope, or preyncope evaluation. The ue of the EER recording a a convenient upport for LTM wa decided upon after conideration by medical taff. From a clinical point of view, the interet of the hort recording of

4 Senor 7, 7, of 3 EER in thi cenario i twofold. On the one hand, they are a real problem in clinic, a thee device are likely to be working intenely and ued during the next year. On the other hand, they almot certainly elect the mot intereting egment of the continuou ignal, either for being real arrhythmia or for being artifact, that are conidered a arrhythmia by the device. In the econd cae, thee egment repreent noie with imilar propertie to the arrhythmia, which i what we wih to claify correctly (for intance, 5 Hz noie and a contant line for amplifier aturation can be more readily detected, and thee do not hold much clinical interet). Our view i that if we are able to dicriminate well between the noie in thee EER recording, it will be eaier to work with LTM in Holter recording, which often include le-noiy ignal region to the global patient map. A continuou and exhautive claification and labeling of noie everity in one 7 day Holter recording wa alo made. The mean left-ventricular ejection fraction (LVEF) in thi databae wa 36.6 ± 9.6%; 86.8% of patient were in the New York Heart Aociation (NYHA) cla I or II and their age were 54. ± 3.9 year. All the record were continuouly regitered at a ampling frequency of 8 Hz. Noie clinical everity wa evaluated in only out of the 53 heart failure patient of the databae becaue of the high complexity and time-conuming proce of labeling (ee [] for more detail). More than, creen from thi recording were manually upervied and labeled by a trained expert for more than 5 h. Wherea a ingle 7 day patient can be een a limited and could have too patient-pecific noie, thi approach repreent a trade-off between viability and cope of the preent tudy. The time required for the continuou labeling of the 7 day Holter wa about 5 h, which wa around day of continuou work only to label and effectively a longer period than thi for a reearcher workload. Hence, we conidered that thi firt work wa neceary, a far a that it would provide u with relevant conideration to be taken into account for effectively developing the gold tandard. If we wih to expand the gold tandard in the future to a wider et of 7 day Holter recording, then it i neceary to have a tarting point that i informative enough to later make it viable and efficient from a human reource viewpoint. Neverthele, a a comparion to the cope provided by the EER databae, the MIT Noie Stre databae [3,33] i often ued a the gold tandard in thi environment (although with different purpoe of algorithm tuning). Thi widely ued databae conit of about 6 h, with egment of half hour in two patient (8 and 9) and artificially added noie with ix different SNR. In thi work, only in EER did we build more than 6.5 h of detailed and continuouly time labeled egment in patient; hence both databae are comparable in term of duration, and the preent databae ha an improved cope in term of repreenting different patient... Noie Clinical Severity Thi work deal with the quality of the ECG, under the aumption that good quality mean clinical validity of the ignal, wherea poor quality compromie the clinical value of the ECG for diagnoi upport. According to thi view, and rather than noie elimination, our attention here i devoted to noie quantification for the purpoe of identifying clinically invalid egment to analyze ECG meaurement on them. Therefore, the etablihment of quality criteria with clinical validity i the firt point to be tackled. We defined and validated a et of noie everity criteria in real ECG ignal obtained from EER device in patient (ee [3] for detail on the databae). An expert cardiologit (A.G.A.) made a qualitative decription of the noie level in Holter recording in term of their impact on the clinical diagnoi of baic parameter, uch a the ECG waveform and heart rate ditortion. After everal iteration, a trained expert (E.E.V.) manually labeled the ECG from our patient databae. We note that it would have been ideal to have had two oberver for each of the recording in order to generate the label, but thi repreented a limitation in term of workload. Neverthele, a et of recording wa initially crutinized with the ue of two oberver, and given that there wa a high concordance between them, an agreement could be readily achieved by a et of rule after training the expert in

5 Senor 7, 7, of 3 a repreentative et of complicated example; an iterative proce wa ued to review the doubtful cae together. After thi long iterative proce, we etablihed and applied the following et of criteria to be ued a label in the ECG egment: Noie-free (type ): egment without noie. Low noie (type ): ome noie preent in the egment, but P and T wave (correponding to atrial deporalization and ventricular repolarization, repectively) and the QRS complexe are readable and their morphology can be identified. Moderate-noie (type ): noiy egment in which only the QRS complexe are reliably identified, in at leat three conecutive beat. Hard-noie (type 3): noiy egment with hardly recognizable or unrecognizable QRS complexe. Other noie (type 4): egment are calibration pule or traight line becaue of the complete abence of ignal or amplifier aturation. It can be een that thee propoed noie criteria are baed on the clinical impact of the noie on the parameter to be meaured in the ECG, independently of power noie and ignal magnitude decription. We noted that thi final noie claification wa cloely related to thoe provided by other reference (e.g., [7]). However, and in contrat to other work, our approach created a continuouly running labeling for all the time of all the ignal in our databae. Therefore, after achieving uniform criteria and claifying all the EER egment in thee type of noie, we obtained a running gold tandard for noie taxonomy in term of it clinical everity..3. Noie Meaurement uing Quantitative Metric In order to crutinize the impact of the noie on the ECG clinical interpretation, we alo analyzed the relationhip between noie intenity and clinical everity. The meaure of the ditortion in an ECG recording wa undertaken here for three type of noie, namely, baeline wander (BW), powerline interference (PLI), and tandard deviation noie (SDN). BW and PLI were choen becaue they are the mot uual type of noie in cardiac record, and they are alo eay to extract from the ECG ignal. In addition, SDN i a novel meaure propoed in thi work, which wa deigned to take into account event that make the ignal unreadable, uch a ignal lo or gain aturation due to electrode diconnection. Among the exiting method to quantify noie, the following implementation were ued here:. BW wa calculated by uing a cubic pline with a third-order polynomial interpolation and a.8 time window for node etimation.. The quantification of PLI wa made with a notch filter with the center frequency at 5 Hz. 3. The propoed SDN wa extracted by following thee tep: (a) the tandard deviation of the ignal wa computed in block of.5 ; (b) every block, the mean and the tandard deviation were calculated; (c) finally, the mean plu twice the tandard deviation wa ued a a meaure of the noie for each block. Figure how everal illutrative etimated example of thee noie type. We note that in general, thee are not in fact independent from each other, a BW i partly included in the SDN calculation, and vice vera. In any cae, we ued thee to crutinize the different quantitative meaurement in term of the expected quality and to compare them with the noie clinical everity. Once the meaurement of noie intenity are available, either from the clinical everity criteria or from the three quantitative metric, the ECG ignal can be characterized in term of it ditortion. The noie characterization wa tackled from two complementary viewpoint, namely, time ditribution and amplitude ditribution. The method for the time and amplitude characterization are explained next.

6 Senor 7, 7, of (a) (b) (c) Figure. Illutrative example of etimated noie for the different quantitative type analyzed in thi work. Each panel (a c) how two ub-panel, one for the cardiac ignal extracted from the event recorder, and another for the noie of different kind and etimated from that ame ignal: (a) cardiac ignal with ignificant preence of powerline interference (PLI) noie; (b) cardiac ignal when relevant baeline wander (BW) noie i preent; (c) cardiac ignal when tandard deviation noie (SDN) noie exhibit high value. Axi repreent ignal amplitude (vertical, in ) v. time (horizontal, in )..4. Time Characterization with Noie Map We propoe to tackle the characterization of the noie everity in the time domain by mean of a new graphical repreentation, to be called noie map. A noie map allow u to look at the quality of an ECG at a glance; thu we are able to crutinize the extent to which the quality of the ignal change with time, and thu a noie map jut depict the noie level in a given ECG recording over time and for all the recording time.

7 Senor 7, 7, of 3 Noie map can be ued when the noie intenity i determined either by clinical or quantitative criteria. In both, the following tep are followed to build a noie map from an ECG recording: Firt, the ECG ignal i divided into egment, which are labeled according to their noie power; the label for clinical everity were defined in Section.. Afterward, quantitative noie i plit into four unevenly ditributed level, which in thi work were adjuted in the event recorder noie ditribution in order to match the quantitative noie map a cloely a poible for an expert oberver (FMM) to the clinical everity noie map. Thi action generate a egmentation on the ECG, for which a lit need to be tored that include the reference number of each egment, it tarting and ending time, and it noie everity label. Finally, label category change are ued for the time intant to define different-ize egment correponding to the et of ample with the ame label. A a reult, a noie map i the depiction of thee label egment a a function of continuou time. We note that, for the quantitative noie meaurement, the intantaneou noie can be readily defined and ued a the bai for the noie map. An example of a noie map can be een in Figure, which correpond to a 75 ECG excerpt taken from an EER and labeled according to noie clinical everity criteria. Every heartbeat from the ignal i tagged with a ingle label; hence, the label egment do not overlap each other. Thi repreentation report reaonably well on the noie everity evolution over the recording. In thi cae, the everity range from noie-free (type ) up to hard-noie (type 3). More than half of the ECG excerpt i labeled a type (moderate-noie) or 3 (hard-noie). Thee egment tand for noiy block, which can carcely be ued for a reliable analyi of the full heartbeat. Therefore, and depite the hort duration, the example correpond to a noiy ECG when analyzed from a clinical viewpoint. Thi i conitent with the characteritic of the EER databae becaue, a mentioned before, the reult of an EER acquiition i the continuou recording of either arrhythmic or noie event, which can be likely labeled within ome of the previouly defined noie categorie. Amplitude () ECG ignal Time () Patient Lead Qual noie Time () Figure. Noie map repreentation (bottom) of the clinical everity of noie oberved in a 75 electrocardiogram (ECG; top). Horizontal line extend during the time period for which each egment ha been labeled by an expert. There are noie-free egment in blue (type ) and egment in green, where the P and T wave, a well a the QRS complexe, are readable (type ). In yellow egment, only the QRS complexe can be reliably identified (type ), wherea egment with hardly recognizable QRS complexe, in red (type 3), are thoe for which no clinical parameter can be trutfully meaured becaue of evere noie.

8 Senor 7, 7, of 3.5. Statitical Characterization of the Noie Amplitude Beide the repreentation of noie ditribution over time, noie map are alo conceived to diplay coincidence between clinical everity and quantitative noie. Nonethele, it may be conidered that noie everity, when defined from a clinical point of view, can be omehow related to the quantitatively determined noie power. For thi reaon, the quality analyi of the ECG i next accomplihed through the analyi of the naive amplitude, defined here a the abolute value of the etimated intantaneou noie. Let p (n) tand for the ECG noie probability denity function (pdf), n(t), and let p (n v ), v = {bw, pli, dn}, be the pecific noie pdf for the previouly ued quantitative component. We note that we aume that no further noie ource are preent and that they are independent, which will not be true in general; nonethele, thee ditribution are ued here for decriptive purpoe of different noie ource. Then, the following ignal model i ued: x(t) = x c (t) + n bw (t) + n pli (t) + n dn (t) + e(t) () where both x(t) and x c (t) are the noiy and the noiele ECG, repectively; n v (t), v = {bw, pli, dn} refer to the quantitative noiy component; and e(t) i an error term repreenting any other non-conidered noie ource that i till preent in the ignal. In order to further analyze the relationhip between noie clinical everity and quantitative noie, we conider the conditional pdf with repect to the dicrete level of noie clinical everity, defined in Section. and denoted here a l, l, l, l 3 and l 4. Hence, we can define the conditional pdf of each type of noie in term of the noie clinical everity for their comparion, which, for the example of BW noie, i given by p(n bw ) = 4 p(n bw l i )P bw (l i ) () i= where l i denote the type of noie clinical everity, p(n bw l i ) i the conditional ditribution of BW noie with repect to type l i, and P bw (l i ) i the prior probability of that type of noie in the BW noie component. Thee conditional pdf can be imilarly etablihed for the other noie component. 3. Reult 3.. Analyi of the Conditional Ditribution The log-caled hitogram depicted in Figure 3 correpond to the ditribution of the right-hand ide in Equation (), and each curve repreent one level of noie clinical everity. Left (right) panel correpond to noie in EER (7 day Holter) recording. From top to bottom, the ditribution are hown for PLI noie in panel (a,b), for BW noie in panel (c,d), and for SDN noie in panel (e,f). Each plot repreent the value of the quantitative noie conditionally eparated according to the clinical everity label. All the curve repreent the full range of exiting value for the noie in the horizontal axi; thu, we may ee that PLI noie (upper plot) for noie-free egment ha a ditribution confined to.4 in EER and to in 7 day Holter. In both cae, the amplitude ditribution for different level overlap one another; hence, the PLI amplitude alone doe not yield a meaurement of the noie clinical everity. A imilar reult i obtained for BW noie (middle plot). The ditribution of Figure 3c exhibit heavier tail than the previou ditribution, which make it evident that high value of BW noie may not correpond with hard-noie in term of clinical everity. Ditribution for 7 day Holter (Figure 3d) are alo heavy tailed and fairly imilar to thoe of EER. The heaviet-tailed ditribution i the hard-noie ditribution in 7 day Holter, which i almot uniform. Thi fact again prevent u from making a diviion in term of clinical everity jut by uing the BW noie amplitude a a criterion. A imilar ituation occur again with SDN noie for EER (Figure 3e), where the hard-noie cover the full voltage range, hence avoiding the labeling according to clinical everity criteria. Regarding the ditribution of the remaining noie, thee are lower in amplitude and are concentrated in pecific

9 Senor 7, 7, of 3 region, exhibiting blank pace (value in the vertical axi lower than µv) in between. In the 7 day Holter cae (Figure 3f), the noie ditribution coexit in an interval that goe up to the local minimum for the hard-noie ditribution (around ). Thi overlapping mean again that mall variation in the SDN noie can be labeled in different clinical everity categorie. After thi minimum, all the ditribution behave imilarly to thoe in Figure 3e, with the aforementioned incapability of labeling the ignal according to the clinical everity criteria baed on SND noie. We note that the difference in the noie amplitude of the horizontal axi for ditribution of the ame cla of noie are due to the different nature of the data and that normal ECG egment eldom appear in EER record becaue they capture noiy event, wherea many normal inu ECG can be often found in the 7 day Holter record (a) (b) (c) (d) (e) (f) Figure 3. Etimated ditribution (from caled log-hitogram) for the different noie type in external event recorder (EER) (a,c,e) and 7 day Holter (b,d,f) recording. Noie ample (in duration, econd, a multiplied time the ampling period) are repreented in term of the noie with the ame voltage level (in ). The color code i imilar to that for the noie map: noie-free egment blue; low-noie dahed, green; moderate-noie croe, yellow; hard-noie dotted, red; and other noie dah dot, black. Axi repreent number of ample caled to their time duration (vertical, in ) v. amplitude (horizontal, in ).

10 Senor 7, 7, 448 of Reult of Noie Map for EER Table how the duration of the different clinical everity noie for each patient in both lead of the EER and according to their clinical everity, which overall were 3 h 7 m 56 (noie-free, 6.77%), 3 h m 5 (low-noie, 5.67%), 4 h 4 m 5 (moderate-noie, 3.5%), h 9 m 8 (hard-noie,.5%), and 53 m 7 (other noie, 6.88%). Given that the quantitative noie amplitude i given a a continuou-variable function, a et of label mut be determined in order to preent thee a noie map. We ued label to repreent noie-free, a well a low-, moderate-, and hard-noie egment. To obtain each category, a et of different threhold wa manually et by uing the amplitude hitogram for all the quantitative noie. In order to et the different threhold, the following rule were ued: (a) the noie-free threhold wa et in order to contain the near-zero value, (b) the hard-noie threhold wa et to iolate the heavy tail of the ditribution, and (c) the threhold to eparate the low-noie and the medium-noie wa et in about the middle of the region delimited by the noie-free and hard-noie threhold. The threhold diviion, a well a the hitogram of each kind of noie, are hown in Figure 4. In order to et the threhold value, only the EER hitogram were ued, becaue thee provided u with a wider view of the noie, a the record came from different patient and covered very different ituation related to noie. We note that the horizontal axi ha a much lower magnitude in the PLI noie, a far a that it amplitude i in general much leer than that of BW and SDN noie. However, in all of thee, the ditribution are hown to be motly exponential-like (PLI and BW noie) and with heavy tail, although SDN i lightly different and centered. The threhold were choen on the hitogram region, aiming to eparate the low-amplitude region (cloe to zero amplitude), the heavy-tail region (large amplitude value), and different trend in the ditribution ma morphology to eparate low from medium region Log (n) PLI noie Log (n) BW noie Sample Log (n) SDN noie (a) Figure 4. Cont.

11 Senor 7, 7, 448 of Log (n) PLI noie Log (n) BW noie Sample 6 4 Log (n) SDN noie Figure 4. Hitogram and threhold etablihed for the different quantitative noie type preent in external event recorder (EER) (a) and 7 day Holter (b) recording. Horizontal axi i the noie amplitude (in ) a explained in Section.3, and vertical axi repreent the number of ample per voltage bin. We note that the tail are better viualized in the logarithmic cale for the hitogram of ample count (inider plot in each panel). Axi repreent number of ample (vertical) v. their amplitude (horizontal, in ). (b) The viualization of noie map in long recording can be operatively limited; hence, we alo introduced the ue of noie bar, a een in the example in Figure 5. Noie bar were obtained by aggregating the duration of the ame type of noie in fixed-duration egment, known a time bar, and then normalizing thee in each time bar. We note that the time bar wa 3 in length for EER patient. Thu, a vertical bar tand for one ingle time egment, which how the ratio of each kind of noie preent throughout that time period. The ame color code i ued o that the lower (upper) part of the bar convey the lower (harder) noie level. Thi provide a profile view, giving a good idea of how clean or noiy a recording i and how many and which clinical parameter are poible to meaure through it. Different time egment are eparated by a blank bar for the cae of EER when tored egment are not continuou in time.

12 Senor 7, 7, 448 of 3 Table. Duration () of noie type in lead (left) and (right) for external event recorder (EER; 8, 3 and 7) and 7 day Holter (7d) recording. N. Pat Total Duration Free Low Moderate Hard Other Free Low Moderate Hard Other 843, 383,55 774,36 6,97 57,6 5,88 563,73 984, 44,5 44,87 5,88 433, 95, 79, 994,89 48,4 43,76 8,68 75,48 994,67 488,4 43, , 5,53 37,6 89,7 434,43 68,36 3,5 87,83 483,4 8,95 39, , 338,98 44,54 86,47 39,53 35,49 345,5 3,84 54,5 745,66 35, ,7 345,95 79,36,9 9,76,8 368,7 89,67,9 9,76, , 65,5 6,6 5,3 5,3 38,35 49,65 83,6 8,4 5,4 379,8 7 7, 376, 356,4 394,6 37,7 7,67 43,4 364,3 357,38 39,4 7, , 77,8 366,84 56,3 5,3 596,76 5,6 34,99 4,4 884,84 596,6 3 78, 3,38 5,94 347,98 86, 3,5 3,38 5,94 347,98 86, 3, , 89,45 76,5 483,8 48,6 5,88 89,45 76,5 483,8 48,6 5,88 7d ,99 537,7 355, 5, 6468,7 3636,4 546,9 355,3 5, 58755,5 N. Pat: denote number of patient.

13 Senor 7, 7, of 3 Qual noie PLI noie BW noie SDN noie Patient Lead Time map (bar = 3 econd) (a) Qual noie PLI noie BW noie SDN noie Patient 3 Lead Time map (bar = 3 econd) Figure 5. Example of external event recorder (EER) noie bar to compare qualitative and quantitative noie. Each bar repreent 3 egment of the recording, for patient (a) and patient 3 (b). (b) Figure 5 how the noie bar of patient and 3, for noie clinical everity in term of PLI, BW, and SDN (from top to bottom). In patient, according to the noie clinical everity (upper plot), all the clinical meaurement are allowable (noie-free, in blue) except for a mall ection at the beginning (in red, or 3 bar) and another predominantly yellow ection (5 to and 65 to 8 bar), where only QRS are morphologically recognizable. In contrat to the gold tandard, if the noie analyi were baed only on PLI (econd plot), an area defined to be allowable according to the clinical tandard would have been labeled a uele for meauring morphological parameter (yellow ection). If we only look at the BW map (third plot), the firt ection would have been tagged a non-uable, which i not true according to clinical everity. The ret of the recording would have been identified a valid, which would have caued a wrong parameter meaurement in thoe area for which the clinical everity map i mainly labeled in yellow. If we analyze the quality according to the SDN noie map (lower plot), mot of the recording would be valid, except for a couple of mall area tagged in red. However, thee

14 Senor 7, 7, of 3 unacceptable area in term of quality can indeed be ued to meaure the morphological parameter. Given thee difference, none of the combination of the three method provide the definition produced by the gold tandard. Similar obervation can be tated regarding patient 3. In thi cae, the gold tandard advie u of a more ditorted ECG than that of patient. For example, the noie map from SDN and BW inform incorrectly that the ignal ha long noiele period (type, in blue), wherea the clinical everity map indicate that only the QRS complex i morphologically recognizable (type, in green). Figure 6 how the noie bar for all of the eight EER patient with the four noie type, allowing u to crutinize the correpondence among different noie type in thi patient population. Panel (a) how the gold tandard noie bar for each patient, and thee allow u to ee that ome recording have very good quality; hence, thee provide an excellent bai for meauring both waveform parameter and rhythm parameter baed on the cardiac cycle, particularly in patient and 5, for whom motly green and blue bar are predominant. We alo have quetionable quality recording for patient, 4, and 8, for whom red and black bar are predominant; hence meaured parameter in thee recording hould be either dicarded or dealt with uing extreme caution. Finally, we were able to meaure the cardiac rhythm mot of the time in patient 3, 6, and 7 (motly yellow and green bar), but in thee cae, morphological parameter were either avoided or dealt with uing caution. Blank bar indicate the cae for which the tored EER egment were not continuou in time, given that thee recording were triggered either by arrhythmic event or noie artifact, wherea in 7 day Holter recording, paue were often recorded through the ioelectric line and can be een motly in black bar. Panel (b) how the noie bar according to the BW noie, and it concordance with panel (a) i low. For intance, mot of the patient have a tronger preence of blue and green bar, which hould promote the calculation of all the clinical parameter in all the patient and i mileading. Similar concluion could be drawn in panel (c) and (d) for PLI noie and SDN noie. We note alo that, for the three quantitative cae, region identified a non-uitable for meauring clinical parameter (motly red bar) are generally different from each other, and are motly different from the gold tandard..5 Patient.5 Patient Patient Patient Patient Patient Patient Patient 3 Noie ratio Time map (bar = 3 econd) Patient 5 Noie ratio Time map (bar = 3 econd) Patient 7 (a) (b).5 Patient.5 Patient Patient Patient Patient Patient Patient Patient 3 Noie ratio Time map (bar = 3 econd) Patient 5 Noie ratio Time map (bar = 3 econd) Patient 7 (c) (d) Figure 6. Cont.

15 Time map (bar = 3 econd) Patient Patient Patient 3 Patient 4 Patient Time map (bar = 3 econd) Patient 6 Patient 7 Patient 8 Patient 3 Patient 7 Senor 7, 7, of 3.5 Patient.5 Patient Patient Patient Patient Patient Patient Patient 3 Noie ratio Time map (bar = 3 econd) Patient 5 Noie ratio Time map (bar = 3 econd) Patient 7 (e) (f) Noie ratio Noie ratio (g) (h) Figure 6. Noie bar of all the analyzed noie type for every patient in the external event recorder (EER) databae: (a,b) noie clinical everity according to the gold tandard; (c,d) baeline wander (BW) noie component; (e,f) powerline interference (PLI) noie component; and (g,h) tandard deviation noie (SDN) component. Blank bar indicate thoe cae for which the tored EER egment are not continuou in time Specific Conideration of PLI Noie A een in Figure 4, the hitogram of the etimated PLI noie could give the reader the idea of extremely low-noie power being preent in the preent databae. We would like to emphaize that the method ued for etimating the PLI noie eentially conited of a notch filter and the ubtraction of the filtered ignal from the unfiltered ignal. Thi mean that, when no PLI noie wa preent, an extremely mall reidual wa extracted, and if the recording had long PLI-noie-free period, thi would repreent noticeable denity ma with low-noie amplitude. Moreover, thi PLI imple etimation method i enitive to wide-band artifact, uch a electrode diconnection, aturation, and other. However, it would uffice to include here a quantitative etimation of PLI preence in the recording for the preent work, focuing on clinical everity of the noie. Figure 7 how two ignal example, both on the dependence of the etimated PLI noie with other noie ource, and on the amplitude hitogram and the relevance of it tail when the noie i not preent throughout the ignal. The difference between a high- and a low-pli-contaminated egment can be oberved in panel (a), where lower noie i preent in the etimated 5 Hz noie in the firt half but i due to artifact, and much clearer noie i preent in the econd half, which i due to the true preence of 5 Hz noie. In panel (b), there i no 5 Hz noie preent, o that the etimated PLI noie exhibit a low amplitude, and in thi cae, the hitogram ha intene ma denity in very low noie amplitude. From oberving thee partial-ignal amplitude hitogram, one can note that the ignificant information in thi kind of noie i found in the tail, and that the cleaner the databae, the more the pdf ma will concentrate on extremely low amplitude. Thi i one of the reaon for repreenting the overall pdf with a logarithmic cale in the preceding figure.

16 Senor 7, 7, of 3 Original Signal Notch-Filtered Signal time () PLI noie time () PLI noie ditribution.5 3. Sample time () amplitude () (a) Original Signal Notch-Filtered Signal time () PLI noie time () PLI noie ditribution Sample time ().5..5 amplitude () (b) Figure 7. Additional conideration of the etimated powerline interference (PLI) noie: (a) example of external event recorder (EER) egment with high preence of PLI noie; (b) example of EER egment with all kind of quantitative noie. We note alo that the threhold that are ued in thi work are not et intending to be univerally etablihed, but intead, we imply wihed to how a quantification of the PLI noie, together with the other noie, and ue thee in the available recording to crutinize their correpondence with the clinical everity criterion. The ue of other threhold beide thoe herein would be recommended in term of different application. Neverthele, after noting that there wa not a great amount of PLI noie in the ued recording, we analyzed two new cae of EER (labeled here a patient 3 and 7), which were elected according to a noticeable preence of their PLI noie. Their noie bar can be een in Figure 6 and are compared with the other cae; it could be confirmed that the PLI noie wa repreented by exiting record in the previou dataet Conideration of Inter-Oberver Variability Aiming to have ome information on the variability of the inter-oberver criteria, we obtained the concordance of the two newly available recording ued in the preceding ubection. Another author (FMM) wa trained to follow the deigned criteria and labeled patient 3 and 7 according to thee.

17 Senor 7, 7, of 3 Wherea thi repreent an a poteriori analyi of the inter-oberver variability, it till brought into cene ome relevant conideration. Table how the confuion matrix for the concordance between both labeler (only for lead ) in thee recording. We can ee that type 3 and 4 are extremely concordant, wherea type are le concordant; thi i an expected reult becaue of the partly ubjective nature of the labeling. We note that the greater difference take place with neighbor everity type, a fact that wa previouly oberved by other author. For intance, a imilar obervation in [7] wa olved by accounting for neighbor clae in the concordance meaurement. We preferred here to retain the etablihed claification and note that the concordance i in general high, a quantified by the Cohen kappa coefficient of.6987 [34]. We further note that type and (no noie and low-level noie) could even be put together in the ame cla, a they both yield good quality clinical indice with either no proceing or with very light proceing. In thi cae, and for informative purpoe only, the confuion matrix i given in the ame table when we put together type and, and the Cohen kappa coefficient i raied up to.77. Thi lat reult implie that, given the cot of the continuou labeling, in combination with the impact of conidering type and a eparate on the meaurement, it could be recommended to put together thee two clae from a practical point of view. Table. Confuion matrice, in minute, for Cohen kappa coefficient in egment for external event recorder (EER) patient 3 and 7, for the etablihed claification of clinical noie intenity (up) and after merging noie-free and low-noie clae (type and ). Oberver Free Low Moderate Hard Other Free Low Oberver Moderate Hard Other Oberver Oberver Free-Low Moderate Hard Other Free Low Moderate Hard Other Reult of the 7 Day Holter Cae In the 7-day Holter recording, the following amount of noie were found (Table ): 9 h 4 m 35 (noie-free, 5.76%), 5 d 8 h 54 m (low-noie, 8.4%), h 5 m 35 (moderate-noie,.7%), m 5 (hard-noie,.%) and 6 h, 4 m 5 (other noie, 9.9%). Difference were oberved in the hape of their hitogram (Figure 4. For intance, there wa le PLI noie in the 7 day Holter cae (much narrower hitogram). In addition, the BW and SDN ditribution, which had their larget probability cloe to zero in the EEG, had cloe to a non-zero mean value in the 7 day Holter. Thi wa checked to have happened becaue in thi ingle cae, the noie level often remained at a quai-contant level. Even o, the width of the hitogram were qualitatively comparable, although with a lower tail for extreme amplitude in both type of noie in the Holter cae. Neverthele, the ame threhold obtained from the EER recording were ued for the 7 day Holter cae, a the former repreented a wider population, to avoid overfitting to one ingle hitogram. Figure 8 how the noie map for all the noie from thi LTM recording. A comparion between quantitative noie bar and the gold tandard how that we could extract morphological parameter and cycle with clinical reliability in virtually all of the recording, except for the black ection, which correpond to diconnection and recharge during day 7. Compared with the PLI noie

18 Senor 7, 7, of 3 map, the whole record i motly free of network noie, jut a we aw in the firt hitogram of Figure 4b, for which, in thi record, there wa hardly any noie of thi type. In the cae of BW, the noie map are overall in compliance with the gold tandard regarding the low noie level (green), indicating that all the clinical meaurement could be motly taken. Neverthele, we could dicard a good number of ection (in yellow) of morphological parameter, without the need for thi. The SDN noie map how an extremely changeable behavior. It incorrectly marked valid black region (thoe of the beginning), and it alo incorrectly labeled a invalid (in red) at leat three large region. Accepting that in thi cae, the BW could be a relatively acceptable proxy to the gold tandard, the populational characteritic of the EER recording ugget that a wider population in 7 day Holter recording exhibit larger diperion and dicordance, even for BW color bar. Finally, Figure 9 how the front-end of our propoed noie map when ued with the 7 day Holter. The firt panel how the noie bar for the entire Holter, and the egment of different everity according to the gold tandard are hown in the econd panel. A high preence of low noie in more than 8% of the recording (in green) allow to meaure all the clinical meaurement, both in morphology and rhythm. The third row with two plot how noie bar and their correponding noie map to meaure the amount of BW, PLI or SDN component, o that qualitative veru quantitative comparion can be performed to how the ignificance of the ECG clinical ditortion. The lower panel how the color-labeled ECG egment, which can be navigated on the econd row and adjuted in time length. Qual noie PLI noie BW noie SDN noie Patient 7D Lead Time map (bar = hour) Figure 8. Example of noie bar for one lead of the 7 day Holter cae. Each bar repreent h of recording (total of about 7 h).

19 Senor 7, 7, of 3 Figure 9. Noie map interface of the 7 day Holter recording (lead ). See text for detail. 4. Dicuion In thi work, attention ha been paid to noie quantification with the purpoe of identifying clinically invalid ECG egment rather than noiy egment elimination, in term of the clinical parameter (morphology and rhythm) that can be reliably meaured from them. For thi purpoe, we firt propoed a four-type criterion of noie clinical everity. The concept of noie map ha been defined a the repreentation of the temporal ditribution of the noie proportion with qualitative level, a well a with other dicrete magnitude noie everity level a aigned for different quantitative type of noie. Wherea the labeling proce ha been extremely time-conuming, the proof of concept how that the noie map with the clinical criteria how conitency with time and with tatitical amplitude ditribution. Hence, thi work preent a olid bai, to be extended in the future for training intelligent ytem capable of automatically determining the noie level everity from a complete ECG recording. Our propoal i different from other noie decription baed only on the level of the SNR etimated by mathematical metric. When ECG ignal are filtered to eliminate noie component (e.g., low and high frequencie), the ECG morphology-baed magnitude, which are uually ueful for detecting cardiac pathologie, could be modified or ditorted by thee filter. Mot of previou effort in the ECG-noie tudy domain focu on the mitigation or removal of the noie in ECG recording from public databae, for example, MIT-BIH Arrhythmia or Noie Stre Tet databae. Differently from our preent reearch, thee databae recording are not continuouly labeled in term of their ignal quality. In addition, thee tudie eldom decribe a detailed noie cale claification, but only the uual BW, mucle artifact, and PLI noie power [35 38], and controlled noie with different SNR value i often added to the ECG ignal [39 4] or i aigned in global term of acceptable or unacceptable ignal for the ECG quality aement [9,4,43]. To our bet knowledge, the concept of noie map with noie everity criteria ha never been raied. In the carcity of publihed evidence to upport noie clinical everity decription, method are mainly deigned with the purpoe of teting the denoiing algorithm propoed by their author. For example, the MIT-BIH Arrhythmia databae wa ued in [7] to train a upport vector machine claifier to perform quality claification of clean ECG ignal with added noie of three different kind, namely, mucle artifact, BW interference, and electrode motion, with different SNR level.

20 Senor 7, 7, 448 of 3 Similarly to our work, they identified four type of quality level noie (minor, moderate, evere, and extreme). In [44], the cardiologit manually re-annotated the quality of 5 - recording and 5 6-min recording, and they identified three type of noie from an energy point of view, namely, low-, medium-, and high-energy noie. In [4], ix noie corruption level were etablihed, from no-noie to noie-poitive detection, according to ECG amplitude and lope in different frequency band). In [45], the author decribed a mathematical algorithm for noie level etimation baed on threhold and comparion with R-wave. None of thee tudie crutinized the tatitical and temporal ditribution of a clinically etablihed gold tandard for the noie everity. It ha been pointed out previouly that noie component in one application could be interpreted a ECG ignal in other, o that SNR can be an inexact meaurement for clinical quality [46]. Although everal tudie have been publihed with 4 h Holter recording, only ome egment of a few minute are conidered to tet different algorithm, and few detailed noie decription can be found [35]. With the recent increae in wirele mobile wearable and LTM clinical device, the neceity of analyzing continuou egment i today even more evident [4]. Our reult how that thi i poible from a tatitical and time-evolution point of view. Study limitation. In thi work, we have made an effort to aemble a databae with continuou and detailed labeling. Thi pecification can be difficult to merge with LTM, a intenive labeling over 7 day Holter turned into an extremely time-conuming tak, a pointed in the method ection. We aimed to overcome thi limitation by working on EER, which till wa a time-conuming tak, but to a leer extent. The workload required to obtain a continuou labeling wa one of the major challenge that we faced throughout the proce. Neverthele, it allowed u to create a reaonably wide databae from which to tart, a et of tool, and the interdiciplinary workflow to expand it. Future work will be devoted to uing thi high-quality and continuou databae a the bae for larger tudie, likely by uing machine learning technique and emi-upervied working flow, uch a thoe provided by incremental learning technique. 5. Concluion ECG Holter recording are commonly corrupted by noie. If ECG ignal are filtered to eliminate thee noie component, the ECG morphology and rhythm meaurement can be modified and may ditort accurate clinical claification. We propoe a meaurement of the noie clinical everity (noie map) to be ued for analyzing conventional quantitative noie meaurement. In contrat to other tudie, which add artificial noie to a clean baeline ECG, our approach ued a real databae with real noie (a ingle 7 day Holter and EER recording), and both the propoed criteria and the noie map were baed on the clinical need for ECG interpretation. We conclude that their ue i a highly deirable direction to improve the data quality when analyzing the meaurement of long-term cenario, uch a EER recording or 7 day Holter monitoring. With the relevance of the emerging modalitie of LTM in current health and wellne cenario nowaday, thi repreent a current neceity. Acknowledgment: Thi work wa upported by the PRINCIPIAS project (TEC C4--R and TEC C4--R), FINALE project (TEC6-756-C--R and TEC6-756-C--R), KERMES project (TEC6-89-REDT), and project TEC C3--R with FEDER funding. Thee grant allow to cover open acce cot for publication. Author Contribution: J.L.R.-A. and A.G.-A. propoed the clinical and technical definition of noie map. A.G.-A. uggeted the clinical everity of noie criteria and provided the ECG databae. E.E.-V. performed the manual labeling of all the recording and collected the literature related to ECG noie. The oftware tool wa initially developed by S.S.-P. and wa then advanced and completed by F.M.M.-M. and F.J.G.-B. In addition, A.G.-A., J.L.R.-A., F.J.G.-B. and M.B.-V. reviewed the manucript and managed the proce. All author contributed to the writing of the manucript. Conflict of Interet: The author declare no conflict of interet.

21 Senor 7, 7, 448 of 3 Reference. Sornmo, L.; Laguna, P. Bioelectrical Signal Proceing in Cardiac and Neurological Application; Elevier Academic Pre: Burlington, MA, USA, 5.. Holter, N.E. New method for heart tudie. Science 96, 34, Lee, K.; Choi, Y.Y.; Kim, D.J.; Chae, H.Y.; Park, K.; Oh, Y.M.; Woo, S.H.; Kim, J.J. A Wirele ExG Interface for Patch-Type ECG Holter and EMG-Controlled Robot Hand. Senor 7, 7, 888, doi:.339/ Leth, S.; Hanen, J.; Nielen, O.W.; Dineen, B. Evaluation of Commercial Self-Monitoring Device for Clinical Purpoe: Reult from the Future Patient Trial, Phae I. Senor 7, 7,. 5. Clifford, G.D.; Behar, J.; Li, Q.; Rezek, I. Signal quality indice and data fuion for determining clinical acceptability of electrocardiogram. Phyiol. Mea., 33, Free, C.; Phillip, G.; Galli, L.; Waton, L.; Felix, L.; Edward, P.; Patel, V.; Haine, A. The Effectivene of Mobile-Health Technology-Baed Health Behaviour Change or Dieae Management Intervention for Health Care Conumer: A Sytematic Review. Plo Med. 3,, e Jabaudon, D.; Sztajzel, J.; Sievert, K.; Landi, T.; Sztajzel, R. Uefulne of Ambulatory 7-Day ECG Monitoring for the Detection of Atrial Fibrillation and Flutter After Acute Stroke and Tranient Ichemic Attack. Stroke 4, 35, Dagre, N.; Kottkamp, H.; Piorkowki, C.; Wei, S.; Arya, A.; Sommer, P.; Bode, K.; Gerd-Li, J.H.; Krematino, D.T.; Hindrick, G. Influence of the duration of Holter monitoring on the detection of arrhythmia recurrence after catheter ablation of atrial fibrillation: Implication for patient follow-up. Int. J. Cardiol., 39, Pator-Pérez, F.J.; Manzano-Fernández, S.; Goya-Eteban, R.; Pacual-Figal, D.A.; Barquero-Pérez, O.; Rojo-Álvarez, J.L.; Martínez-Martínez-Epejo, M.D.; Chavarri, M.V.; García-Alberola, A. Comparion of Detection of Arrhythmia in Patient With Chronic Heart Failure Secondary to Non-Ichemic Veru Ichemic Cardiomyopathy by Veru 7-Day Holter Monitoring. Am. J. Cardiol., 6, Goya-Eteban, R.; Barquero-Pérez, O.; Caamaño-Fernández, A.; Rojo-Álvarez, J.L.; Pator-Pérez, F.J.; Manzano-Fernández, S.; García-Alberola, A. Uefulne of 7-Day Holter Monitoring for Heart Eate Variability Nonlinear Dynamic Evaluation. In Proceeding of the Computing in Cardiology, Hangzhou, China, 8 September ; pp Clifford, G.D.; Azuaje, F.; McSharry, P.E. Advanced Method and Tool for ECG Data Analyi; Artech Houe: Boton, MA, USA, 6.. Ari, S.; Da, M.K.; Chacko, A. ECG ignal enhancement uing S-Tranform. Comput. Biol. Med. 3, 43, Jenkal, W.; Latif, R.; Toumanari, A.; Dliou, A.; Bâcharri, O.E.; Maoulainine, F.M. An efficient algorithm of ECG ignal denoiing uing the adaptive dual threhold filter and the dicrete wavelet tranform. Biocybern. Biomed. Eng. 6, 36, Wang, Z.; Wan, F.; Wong, C.M.; Zhang, L. Adaptive Fourier decompoition baed ECG denoiing. Comput. Biol. Med. 6, 77, Yadav, S.K.; Sinha, R.; Bora, P.K. Electrocardiogram ignal denoiing uing non-local wavelet tranform domain filtering. IET Signal Proce. 5, 9, Smital, L.; Vítek, M.; Kozumplík, J.; Provazník, I. Adaptive Wavelet Wiener Filtering of ECG Signal. IEEE Tran. Biomed. Eng. 3, 6, Barro, A.K.; Manour, A.; Ohnihi, N. Removing artifact from electrocardiographic ignal uing independent component analyi. Neurocomputing 998,, Chawla, M. PCA and ICA proceing method for removal of artifact and noie in electrocardiogram: A urvey and comparion. Appl. Softw. Comput.,, Sameni, R. Online filtering uing piecewie moothne prior: Application to normal and abnormal electrocardiogram denoiing. Signal Proce. 7, 33, Nguyen, P.; Kim, J.M. Adaptive ECG denoiing uing genetic algorithm baed threholding and enemble empirical mode decompoition. Inf. Sci. 6, 373, Blanco-Velaco, M.; Weng, B.; Barner, K.E. ECG ignal denoiing and baeline wander correction baed on the empirical mode decompoition. Comput. Biol. Med. 8, 38, 3.

22 Senor 7, 7, 448 of 3. Kaergaard, K.; Jenen, S.H.; Puthuerypady, S. A comprehenive performance analyi of EEMD-BLMS and DWT-NN hybrid algorithm for ECG denoiing. Biomed. Signal Proce. Control 6, 5, Reddy, A.S.K.; Chand, N.V.S. ECG Noie Removal by Uing Fuzzy Logic Filter. Int. J. Comput. Sci. Math. Eng. 7, 4, Poungponri, S.; Yu, X. An adaptive filtering approach for electrocardiogram (ECG) ignal noie reduction uing neural network. Neurocomputing 3, 7, Xiong, P.; Wang, H.; Liu, M.; Zhou, S.; Hou, Z.; Liu, X. ECG ignal enhancement baed on improved denoiing auto-encoder. Eng. Appl. Artif. Intell. 6, 5, Ochoa, A.; Mena, L.J.; Felix, V.G. Noie-Tolerant Neural Network Approach for Electrocardiogram Signal Claification. In Proceeding of the International Conference on Compute and Data Analyi, ICCDA 7, Lakeland, FL, USA, 9 3 May 7; pp Li, Q.; Rajagopalan, C.; Clifford, G. A machine learning approach to multi-level ECG ignal quality claification. Comput. Method Prog. Biomed. 4, 7, Redmond, S.J.; Lovell, N.H.; Bailaki, J.; Celler, B.G. ECG quality meaure in telecare monitoring. In Proceeding of the 3th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, BC, Canada, 5 Augut 8; pp Satija, U.; Ramkumar, B.; Manikandan, M.S. A imple method for detection and claification of ECG noie for wearable ECG monitoring device. In Proceeding of the International Conference on Signal Proceing and Integrated Network (SPIN), Noida, India, 9 February 5; pp Ever-Villalba, E.; Melgarejo-Meeguer, F.; Gimeno-Blane, J.; Sala-Pla, S.; Blaco-Velaco, M.; Rojo-Álvarez, J.; García-Alberola, A. Clinical Severity of Noie in ECG. In Proceeding of the Computing in Cardiology Conference (CinC), Vancouver, BC, Canada, 4 September 6; pp Palma-Gámiz, J.L.; Arriba, A.; González-Juanatey, J.R.; Marí-Huerta, E.; Simarro, E. Guía de práctica clínica de la Sociedad Epañola de Cardiología en la monitorización ambulatoria del electrocardiograma y preión arterial. Rev. Ep. De Cardiol., 53, 9 9. (In Spanih) 3. Moody, G.B.; Muldrow, W.; Mark, R.G. A noie tre tet for arrhythmia detector. Comput. Cardiol. 984,, Goldberger, A.L.; Amaral, L.A.; Gla, L.; Haudorff, J.M.; Ivanov, P.C.; Mark, R.G.; Mietu, J.E.; Moody, G.B.; Peng, C.K.; Stanley, H.E. PhyioBank, PhyioToolkit, and PhyioNet. Circulation,, E5 E. 34. Cohen, J. A Coefficient of Agreement for Nominal Scale. Educ. Pychol. Mea. 96,, Lee, J.; McManu, D.; Merchant, S.; Chon, K. Automatic Motion and Noie Artifact Detection in Holter ECG Data Uing Empirical Mode Decompoition and Statitical Approache. IEEE Tran. Biomed. Eng., 59, Agrawal, S.; Gupta, A. Fractal and EMD baed removal of baeline wander and powerline interference from ECG ignal. Comput. Biol. Med. 3, 43, ElB charri, O.; Latif, R.; Elmanouri, K.; Abenaou, A.; Jenkal, W. ECG ignal performance denoiing aement baed on threhold tuning of dual-tree wavelet tranform. Biomed. Eng. Online 7, 6, Rodrigue, J.; Belo, D.; Gamboa, H. Noie detection on ECG baed on agglomerative clutering of morphological feature. Comput. Biol. Med. 7, 87, He, H.; Tan, Y.; Wang, Y. Optimal Bae Wavelet Selection for ECG Noie Reduction Uing a Comprehenive Entropy Criterion. Entropy 5, 7, Jekova, I.; Krateva, V.; Chritov, I.; Abächerli, R. Threhold-baed ytem for noie detection in multilead ECG recording. Phyiol. Mea., 33, Sivakumar, R.; Tamilelvi, R.; Abinaya, S. Noie Analyi and QRS Detection in ECG Signal. In Proceeding of the International Conference on Computer Technology and Science (ICCTS ), New Delhi, India, 8 9 Augut ; pp Clifford, G.D.; López, D.; Li, Q.; Rezek, I. Signal quality indice and data fuion for determining acceptability of electrocardiogram collected in noiy ambulatory environment. Comput. Cardiol., 68, Satija, U.; Ramkumar, B.; Manikandan, M.S. Automated ECG Noie Detection and Claification Sytem for Unupervied Healthcare Monitoring. IEEE J. Biomed. Health Inf. 7, PP, doi:.9/jbhi Naeri, H.; Homaeinezhad, M. Electrocardiogram ignal quality aement uing an artificially recontructed target lead. Comput. Method Biomech. Biomed. Eng. 4, 8, 6 4.

23 Senor 7, 7, of Jovanovic, B.; Litovki, V.; Pavlovic, M. QRS complex detection baed ECG ignal artefact dicrimination. Facta Univ. 5, 8, Hayn, D.; Jammerbund, B.; Schreier, G. Noie detection on ECG baed on agglomerative clutering of morphological feature. Phyiol. Mea., 33, c 7 by the author. Licenee MDPI, Bael, Switzerland. Thi article i an open acce article ditributed under the term and condition of the Creative Common Attribution (CC BY) licene (