Sleep Heart Rate Variability Analysis and k-nearest Neighbours Classification of Primary Insomnia

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1 INTERNATIONAL JOURNAL OF INTEGRATED ENGINEERING VOL. 10 NO. 7 (2018) Unverst Tun Hussen Onn Malaysa Publsher s Offce IJIE Journal hoepage: The Internatonal Journal of Integrated Engneerng Sleep Heart Rate Varablty Analyss and k-nearest Neghbours Classfcaton of Prary Insona Haslale Abdullah 1,*, Thoas Penzel 2, Dean Cvetkovc 3 1 Razak Faculty of Technology and Inforatcs, Unverst Teknolog Malaysa, Jalan Sultan Yahya Petra, Kuala Lupur, 54100, MALAYSIA 2 Charté Unverstätsedzn Berln, Chartéplatz 1, Berln, 10117, GERMANY 3 RMIT Unversty, Melbourne, 3001, AUSTRALIA *Correspondng Author DOI: Receved 26 October 2018; Accepted 19 Noveber 2018; Avalable onlne 30 Noveber 2018 Abstract: The Heart Rate Varablty (HRV) of any sleep dsorders shows an alteraton of the sypathovagal balance of the Autonoous Nervous Syste (ANS). Prary nsona refers to the dffculty n ntatng or antanng sleep that s not caused by other llnesses or substances. The HRV of prary nsona shows nconsstent fndngs although t s beleved to par the HRV varables. Ths study copares the HRV changes durng dfferent sleep stages and evaluates the k-nearest neghbours (knn) classfer usng the HRV features for prary nsona classfcaton. The te and frequency HRV varables were extracted fro sleep ECG sgnals of 10 prary nsona patents and 10 healthy controls durng four sleep stages N1, N2, N3 and REM. The Mann- Whtney U-test was conducted to evaluate the exstence of statstcal sgnfcant dfferences between the two groups at dfferent sleep stages. The knn classfer was adapted for the classfcaton tool. Only the LF ndex of HRV was sgnfcantly hgher n the prary nsona patents copared to the healthy subjects. The classfcaton accuracy of knn was at 75% when both the HRV te and frequency varables were accounted as nputs to the classfer. Keywords: Insona, heart rate varablty, knn, classfcaton 1. Introducton Insona has affected llons of people worldwde wth the key sypto of havng dffculty n fallng asleep or reanng asleep. Generally, nsona can be categorsed nto two types: prary nsona and secondary nsona. Prary nsona s purely not related to any other edcal and health condton, whereas secondary nsona occurs as a consequence of physcal or ental probles (Wlson, 2015). Depresson, shft work and coorbd physcal llnesses have been dentfed as the rsk factors of nsona (Klnk, Quan, Kaltenborn, & Lebowtz, 1992; Sagula, Stone, Fabo, & Cauley, 2016). The pact of nsona, partcularly n cardovascular dseases, ental dsorders and socoeconoc aspects, s sgnfcant (Svertsen et al., 2014). Insona has been reported to ncrease the rsk of developng heart dsease by 45% (Svertsen et al., 2014). It has been estated that the total annual cost of nsona, whch ncludes healthcare servce and edcaton costs and productvty losses, aounts to USD 5 bllon (Daley, Morn, LeBlanc, Grégore, & Savard, 2009). Therefore, the early dagnoss and treatent of nsona are requred to prevent the chroncty and ortalty of untreated nsona. *Correspondng author: haslale.kl@ut.y 2018 UTHM Publsher. All rght reserved. penerbt.uth.edu.y/ojs/ndex.php/je 66

2 Polysonography (PSG) or sleep study that records ultple electrophysologcal sgnals, such as EEG, ECG, EMG and respraton durng sleep, s used as an objectve easure to assess the sleep dsrupton n nsona. Most of the PSG studes have reported shorter total sleep te, lesser slow wave sleep, longer sleep latency and dsrupton n the sleep archtecture (Baglon et al., 2014). In addton to these sleep easures, Heart Rate Varablty (HRV) s another easure obtaned fro the ECG sgnal. HRV s a non-nvasve nstruent and an objectve easure used n clncal research to evaluate the Autonoc Nervous Syste (ANS) n ters of sypathetc and parasypathetc actvtes (Task Force of the European Socety of Cardology the North Aercan Socety of Pacng, 1996). For ore than two decades, HRV has been used as a tool to study ECG because of ts ease and straght-forward pleentaton. It has been reported that patents wth ajor depresson and prary nsona are charactersed by a sgnfcant reducton n the parasypathetc-related HRV (Spegelhalder et al., 2011; De Zabott et al., 2013). Accordng to a recent revew on the cardovascular autonoc actvty n nsona, an ncrease n the LF and/or LF/HF easures and a decrease n the HF and/or SDNN, RMSSD, pnn50 easures has been nterpreted as an ncrease n the sypathetc actvty (Nano, Fonseca, Vullngs, & Aarts, 2017). Nevertheless, ncreased sypathetc and decreased parasypathetc effects on nsona have not been observed by other studes (Dodds, Mller, Kyle, Marshall, & Gordon, 2017; Jurysta et al., 2009). These vared fndngs on the HRV easures reported n the lterature suggest that valdaton studes are needed to deterne whether nsona s related to HRV parent. Clncally, the dagnoss of nsona s conducted through ultple tests, whch nclude questonnares, ntervews, edcal exanatons and sleep studes (Ohayon & Reynolds, 2009). Ths dagnoss technque s not only subjectve but also te-consung. Therefore, an objectve, autoated dagnoss tool s preferred n any sleep dsorder detecton tests. Over the years, a sngle-channel sensor for an autoated syste for sleep-stagng classfcaton (Ylaz et al., 2010) and sleep apnea detecton (Mendez, Banch, Matteucc, Cerutt, & Penzel, 2009; Al-Angar & Sahakan, 2007) has been developed wth hgh detecton rates. Also, ECG sgnals and HRV easures have been used as nputs to varous classfcaton ethods for the autoated dagnoss of cardovascular dseases (Acharya et al., 2017; Fujta et al., 2016). Therefore, the objectve of ths study s to nvestgate the dfferences n the HRV easures durng sleep between prary nsona patents and healthy subjects. Second, ths study explores the knn classfer perforance for prary nsona autoated detecton based on HRV easures. 2. Methodology 2.1 Data In ths study, the PSG data was obtaned fro Charté Unversty (Berln, Gerany). A total of 10 healthy and 10 prary nsona PSG data was analysed. The sleep stages were scored accordngly as N1, N2, N3 and REM fro a 30 s epoch of the PSG sgnal. Sngle-ECG channel (Lead II) sgnals wth a saplng frequency of 200 Hz were extracted fro the PSG data. The whole-nght ECG sgnals were vsually nspected for the deternaton of clean sgnals. Any epochs wth noses and oveent artefacts were dscarded. The ECG sgnals were segented to 300 s wndows for HRV analyss as recoended by the Task Force of the European Socety of Cardology (Task Force of the European Socety of Cardology the North Aercan Socety of Pacng, 1996). The ean and standard devaton for age and sleep paraeters were suarsed as provded n Table 1. Table 1 Subject Age and Sleep Paraeters. Paraeters Healthy Prary Insona Age (years) 36.1 ± ± Total Sleep Te (n) ± ± Sleep Effcency (% ± ± Total Stage Wake (n) ± ± Total Stage N1 (n) ± ± Total Stage N2 (n) ± ± Total Stage N3 (n) ± ± Total Stage REM ± ± Sleep Latency (n) ± ± REM Latency (n) ± ±

3 2.2 Coputaton of Te and Frequency Doan of HRV To copute the te and frequency doans of HRV, the R R te seres need to be generated fro the segented ECG sgnals. The R peaks of ECG sgnals were detected usng the well-known ethod of Pan and Topkns algorths (Pan & Topkns, 1985). The prograng works for the coputaton of HRV easures were carred out usng MATLAB R2017b. 1) Te Doan a) Mean R-R Measures the average te fro beat to beat or the R peak to the successve R peak of the R R te seres. b) SDNN The standard devaton of the R R nterval of noral beats. SDNN s defned as SDNN 1 N 1 N j1 ( RR j RR) 2 (1) where N = a total nuber of successve ntervals. c) RMSSD The root ean square of the successve dfferences between the noral beats. RMSSD 1 N 1 N j1 ( RR j RR) 2 (2) d) pnn50 Ths easure s the nuber of successve ntervals of N N ntervals greater than 50 s. NN50 pnn50 100% N 1 (3) 2) Frequency Doan The Power Spectru Densty (PSD) was easured to estate the R R nterval seres n the frequency doan ethod. PSD easures the absolute power of Hgh Frequency (HF), Low Frequency (LF) and Very Low Frequency (VLF) bands. The frequency range for the LF band s between 0.04 and 0.15 Hz, whch reflects the actvty of the sypathetc part of ANS. The HF band, whch s at a range between 0.15 Hz to 0.40 Hz, reflects the vagal odulaton of cardac actvty. The LF/HF rato defnes the overall assessent of the sypathovagal actvty. We appled a technque based on the Fast Fourer Transfor (FFT) Spectru for PSD analyss. To nse the effect of changes n the total power to the calculaton of HF and LF wthn and between subjects, the noralsed value (unt, n.u) of HRV coponents was coputed as follows: LF nu LF LF HF VLF (4) HF nu HF LF HF VLF (5) LF LF nu / HF rato (6) HF nu 68

4 2.3 Statstcal Analyss The data analyss was perfored usng the Mann-Whtney U-test, where the ndependent varables were the groups prary nsona and healthy and the dependent varables were age and sleep paraeters as shown n Table 1. To further evaluate the exstence of sgnfcant statstcal dfferences between the two groups at dfferent sleep stages, Analyss of Varance (ANOVA) was pleented. The ndependent varables were the groups prary nsona and healthy and sleep stages Wake, N1, N2, N3 and REM whereas the dependent varables were the te and frequency doans of HRV. IBM SPSS software wth p values of 0.05 was used to conduct the statstcal analyss. 2.4 Supervsed Classfcaton: k- Nearest Neghbours (knn) The knn classfer s a supervsed-learnng ethod capable of classfyng the nput data even n the absence of pror knowledge. Its pleentaton s one of the splest pattern recognton ethods, and yet t can acheve hgh detecton accuracy. The knn algorth s based on an ntutve concept, where the dstance between the data n the sae class should be closer to each other. Its coputaton depends on the nuber of k-nearest neghbours and dstance etrcs (Hu, Huang, Ke, & Tsa, 2016). Varous dstance etrcs are avalable to be adapted to solve dfferent classfcaton probles such as Eucldean, Mahalanobs and Mnkowsk (Hu et al., 2016; Mendez et al., 2009). The optal value of k s deterned heurstcally by nspectng the data and cross-valdaton. Generally, a larger value of k proves the overall classfer perforance due to the reducton n the effect of nosy ponts. Fg. 1 llustrates the basc concept of knn where a pont (astersk sybol) s classfed based on the ajorty vote of ts nearest neghbours n the feature space. If k = 3, then the astersk pont s assgned to the dark-crcle group. a* b D Fg. 1 knn Classfer In ths study, the nput vectors that conssted of seven HRV features{x1, X2,..., Xn} were labelled as two groups {1 and 2}. Mn ax noralsaton was pleented to ap the nput vectors nto the range of [0, 1] as X, N X n( X ) ax( X ) n( X (7) ) where X,N s the noralsed th feature of the nput vector. These noralsed nput vectors were further parttoned for tranng (90%) and testng (10%) usng 10-fold cross-valdaton. For the knn classfcaton, the value of k was norally an odd nuber. In ths study, k = 3, 5 and 7 were tested to deterne the optu value that perfored the best n the classfcaton. Four types of dstance etrcs lsted below were aong the preferred etrcs used n the lterature. The calculaton for the dstance etrcs between pont a and b n a feature space was defned as: 69

5 1. Eucldean dstance D 2 ( a, b) ( a b 1 ) (8) where D s the Eucldean dstance between pont a and b. 2. Mahalanobs dstance D 1 ( a, b) ( a b ) C ( a b 1 ) (9) where C s the covarance atrx. 3. Cosne dstance D ( a, b) 1 1 a b 2 2 a b 1 (10) where s the ean value. 4. Correlaton dstance D j ( a, b) 1 1 a b a b 1 (11) The knn perforance was evaluated by easurng ts accuracy, whch s defned as the followng: Accuracy= TN TP 100 (12) TP TN FP FN where TP and TN represent the total nuber of True Postve and True Negatve, respectvely. Also, FP and FN are the total nuber of False Postve and False Negatve counted durng the classfcaton (Baratloo, Hossen, Negda, & El Ashal, 2015). 3. Results and Dscusson The HRV easures of the prary nsona patents and healthy controls were extracted fro fve-nute segents of whole-nght sleep ECG recordng. Mean RR, SDNN, RMSSD and pnn50 were the te-doan HRV features extracted fro the ECG, whereas LFnu, HFnu and LF/HF rato were the frequency doans of HRV. The HRV changes durng sleep stages N1, N2, N3 and REM were copared between the nsona patents and healthy subjects. These te and frequency doans of HRV easures were subsequently used for classfcaton study. The hypnogra of prary nsona and healthy control groups are depcted n Fg. 2. As can be seen n the fgure, patent subjects showed ore frequent awake events (crcle) throughout the nght copared to the healthy subjects. In the assessent of the dfferences n age and sleep paraeters between the two groups, only the REM stage total sleepng te n the prary nsona group was sgnfcantly lower than the healthy group (U = 16.5, p = 0.014). The results for both the te and frequency doans of HRV are shown n Fg. 3 and Fg. 4. The te doan easures of ean RR n the nsona group revealed a slghtly hgher value copared to the healthy group. Ths value showed an ncreasng trend fro stage N1 to REM. However, a sgnfcant dfference was only found n the REM stage. In contrast, the SDNN, RMSSD and pnn50 of the nsona group showed a lower value copared to the control group. The SDNN and RMSSD 70

6 were lower at stage N1 and gradually ncreased at N2, N3 and REM. However, the statstcal test dd not show any sgnfcant dfference n all the sleep stages for SDNN, RMSSD and pnn50. In the frequency doan analyss, the LF easure of nsona was observed hgher when copared to the healthy group. The hghest value of LF was sgnfcantly recorded durng N3 sleep. In contrast, the HF of nsona was lower than the healthy group but not sgnfcant. Also, there was no sgnfcant dfference n the LF/HF ndex between the two groups although the axu value was observed durng the REM sleep n the healthy group. In lne wth several studes, our analyss of the te and frequency doans of HRV n prary nsona patents also showed ncreased sypathetc actvty and reduced parasypathetc actvty of the ANS copared to the control group (Spegelhalder et al., 2011; De Zabott et al., 2013). In ths study, a hgh value of the LF ndex was observed durng the N3 sleep stage n prary nsona patents, whch ndcated the predonance of the sypathetc nervous syste durng deep sleep. Ths ncrease of sypathetc actvty durng sleep was largely hypothessed to cause physologcal hyperarousal and further ncrease the rsk of cardovascular dseases (Carter et al., 2017). Several studes have reported a longer REM sleep te n nsona patents (Reann et al., 2012; Tobaldn et al., 2013). Such duraton of REM sleep s beleved to be assocated wth an ncrease n cro-arousals and awakenng n nsona patents. However, a low duraton of REM sleep te n prary nsona was observed n ths study. Ths varaton could be caused by the sall saple sze used n ths study. a b Fg. 2 - (a) Hypnogra of Prary Insona; (b) Hypnogra of Healthy Subject. Sleep stages annotaton 5 = Wake, 4 = REM, 3 = N1, 2 = N2,, 1 = N3. ** 71

7 Fg. 3 - Mean plot of te ndexes of HRV; (a) RRI; (b) SDNN; (c) RMSSD and (d) pnn50 n sleep stages N1, N2, N3 and REM. ** p<0.05. ** Fg. 4 - Mean plot of frequency ndexes of HRV; (a) LF (nu); (b) HF (nu) and (c) LF/HF n sleep stages N1, N2, N3 and REM. ** p<0.05. In ths study, the optu value of k and dstance etrc was explored. As shown n Fg. 5, the knn accuracy proves as the k value ncreases. The hghest accuracy s acheved usng the Mahalanobs dstance wth k = 7, whereas the lowest detecton rate s observed for the Eucldean dstance etrc and k = 3. The average accuracy of 75.02% s recorded usng knn for the classfcaton of prary nsona and healthy subjects as shown n Table 2. Other dstance etrcs yeld a detecton rate between 65% and 72% wth k value of 3 and 5. Therefore, the perforance of knn classfer s hghly dependent on the tunng paraeters k and dstance etrcs. Although the detecton rate for nsona n ths study s slghtly lower copared to other reported values of 81% (Abdullah, Patt, Dssanyaka, Penzel, & Cvetkovc, 72

8 Accuracy (%) 2017; Mulaffer, Shahn, Glos, Penzel, & Ahed, 2017), our approach s spler, as we have only used a total of seven features extracted fro the ECG sgnal. Mulaffer et al. (2017) used 57 features extracted fro 2 EEG sgnals to obtan an accuracy of 81%. Also, they used SVM that requred several tunng paraeters, whch ncludes kernel functon, gaa and c values. The ltaton of ths study s that a sall saple sze of 20 subjects was used to perfor the statstcal analyss and classfcaton. Secondly, we should have consdered ncludng ore features and then pleentng the feature selecton technque as practced n any achne learnng applcatons Eucldean Mahalanobs Cosne Correlaton k value Fg. 5 Classfcaton accuracy percentage of Eucldean, Mahalanobs, Cosne and Correlaton dstance of k = 3, 5 and 7 Table 2 Average classfcaton accuracy, senstvty and specfcty percentage (%) rates of 10-fold crossvaldaton. Dstance etrc Accuracy Eucldean (k = 7) Mahalanobs (k = 7) Cosne (k = 3) Correlaton (k = 5) Concluson In ths paper, a total of seven features fro the te and frequency doans of HRV between prary nsona patents and healthy subjects were nvestgated n ters of statstcal sgnfcance. Although, the HRV easures of nsona patents have shown an ncrease n the overall of sypathovagal actvty, ths easure alone cannot be regarded as an objectve boarker, as other sleep dsorders, such as sleep apnoea, s also assocated wth an alteraton n the sypathovagal balance. In our experent on the classfcaton of nsona and healthy subjects usng knn and HRV easures, an accuracy of 75% was acheved. Ths result suggests the possble developent of an autoated nsona dagnoss wth contnuous proveent n the feature extracton and classfcaton ethods. 73

9 Acknowledgeent Ths research was funded under the Research Unversty Grant (Vot No. 15H54) fro Unverst Teknolog Malaysa. The authors would lke to thank the Research Manageent Centre of UTM and the Mnstry of Educaton Malaysa for ther fnancal support. References [1] Wlson, L. W. (2015). The Ultate Insona Cure: The Best Soluton to Get Relef fro Insona FAST! [2] Klnk, M. E., Quan, S. F., Kaltenborn, W. T., & Lebowtz, M. D. (1992). Rsk factors assocated wth coplants of nsona n a general adult populaton: nfluence of prevous coplants of nsona. Archves of nternal edcne, 152(8), [3] Sagula, S. F., Stone, K. L., Fabo, A., & Cauley, J. A. (2016). Rsk factors for sleep dsturbances n older adults: evdence fro prospectve studes. Sleep edcne revews, 25, [4] Svertsen, B., Lallukka, T., Salo, P., Pallesen, S., Hysng, M., Krokstad, S., & Øverland, S. (2014). Insona as a rsk factor for ll health: results fro the large populaton based prospectve HUNT Study n N orway. Journal of sleep research, 23(2), [5] Sof, F., Cesar, F., Casn, A., Macch, C., Abbate, R., & Gensn, G. F. (2014). Insona and rsk of cardovascular dsease: a eta-analyss. European journal of preventve cardology, 21(1), [6] Daley, M., Morn, C. M., LeBlanc, M., Grégore, J. P., & Savard, J. (2009). The econoc burden of nsona: drect and ndrect costs for ndvduals wth nsona syndroe, nsona syptos, and good sleepers. Sleep, 32(1), [7] Baglon, C., Regen, W., Teghen, A., Spegelhalder, K., Fege, B., Nssen, C., & Reann, D. (2014). Sleep changes n the dsorder of nsona: a eta-analyss of polysonographc studes. Sleep edcne revews, 18(3), [8] Electrophysology, Electrophysology, Task Force of the European Socety of Cardology the North Aercan Socety of Pacng. (1996). Heart rate varablty: standards of easureent, physologcal nterpretaton, and clncal use. Crculaton, 93(5), [9] Pan, J., & Topkns, W. J. (1985). A real-te QRS detecton algorth. IEEE Trans. Boed. Eng, 32(3), [10] Spegelhalder, K. A. I., Fuchs, L., Ladwg, J., Kyle, S. D., Nssen, C., Voderholzer, U., & Reann, D. (2011). Heart rate and heart rate varablty n subjectvely reported nsona. Journal of sleep research, 20(1pt2), [11] De Zabott, M., Covassn, N., Sarlo, M., Tona, G. D. M., Trnder, J., & Stegagno, L. (2013). Nghtte cardac sypathetc hyper-actvaton n young prary nsonacs. Clncal Autonoc Research, 23(1), [12] Nano, M. M., Fonseca, P., Vullngs, R., & Aarts, R. M. (2017). Measures of cardovascular autonoc actvty n nsona dsorder: A systeatc revew. PloS one, 12(10), e [13] Dodds, K. L., Mller, C. B., Kyle, S. D., Marshall, N. S., & Gordon, C. J. (2017). Heart rate varablty n nsona patents: a crtcal revew of the lterature. Sleep edcne revews, 33, [14] Jurysta, F., Lanquart, J. P., Sputaels, V., Duont, M., Mgeotte, P. F., Lestedt, S., & Van De Borne, P. (2009). The pact of chronc prary nsona on the heart rate EEG varablty lnk. Clncal neurophysology, 120(6), [15] Ohayon, M. M., & Reynolds III, C. F. (2009). Epdeologcal and clncal relevance of nsona dagnoss algorths accordng to the DSM-IV and the Internatonal Classfcaton of Sleep Dsorders (ICSD). Sleep edcne, 10(9), [16] Yılaz, B., Asyalı, M. H., Arıkan, E., Yetkn, S., & Özgen, F. (2010). Sleep stage and obstructve apneac epoch classfcaton usng sngle-lead ECG. Boedcal engneerng onlne, 9(1), 39. [17] Mendez, M. O., Banch, A. M., Matteucc, M., Cerutt, S., & Penzel, T. (2009). Sleep apnea screenng by autoregressve odels fro a sngle ECG lead. IEEE transactons on boedcal engneerng, 56(12), [18] Al-Angar, H. M., & Sahakan, A. V. (2007). Use of saple entropy approach to study heart rate varablty n obstructve sleep apnea syndroe. IEEE Transactons on Boedcal Engneerng, 54(10), [19] Acharya, U. R., Fujta, H., Ada, M., Lh, O. S., Sudarshan, V. K., Hong, T. J., & San, T. R. (2017). Autoated characterzaton and classfcaton of coronary artery dsease and yocardal nfarcton by decoposton of ECG sgnals: A coparatve study. Inforaton Scences, 377, [20] Fujta, H., Acharya, U. R., Sudarshan, V. K., Ghsta, D. N., Sree, S. V., Eugene, L. W. J., & Koh, J. E. (2016). Sudden cardac death (SCD) predcton based on nonlnear heart rate varablty features and SCD ndex. Appled Soft Coputng, 43, [21] Hu, L. Y., Huang, M. W., Ke, S. W., & Tsa, C. F. (2016). The dstance functon effect on k-nearest neghbor classfcaton for edcal datasets. SprngerPlus, 5(1), [22] Baratloo, A., Hossen, M., Negda, A., & El Ashal, G. (2015). Part 1: sple defnton and calculaton of accuracy, senstvty and specfcty. 74

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