ENHANCED SLEEP SPINDLE DETECTOR BASED ON THE FUJIMORI METHOD

Transcription

1 ENHANCED SLEEP SPINDLE DETECTOR BASED ON THE FUJIMORI METHOD Yuka Kawashima, Takashi Yoshida and Naoyuki Aikawa Tokyo University of Science Dept. of Applied Electronics Tokyo, Japan Mitsuo Hayashi Hiroshima University Dept. of Behavioral Sciences Hiroshima, Japan ABSTRACT Afternoon sleepiness in daily life reduces arousal level, performance, and so on. It has been cleared that short naps are effective to cancel the sleepiness. Sleep stage 2 is one of important factors about sleeping especially in short time nap. Sleep spindles are especially important hallmarks of sleep stage 2. Therefore, it is necessary to find a spindle for analysis in sleep stage 2. In this paper, we propose an enhanced sleep spindle detector in time domain. In the proposed method, the sleep spindle waves are detected using the improved Fujimori s method and cumulative sum chart. Through example, the proposed method can detect the sleep spindles with % sensitivity compared with a medical specialist. The proposed method can also extract theta and delta waves. Index Terms EEG, short nap, sleep spindles, timedomain analysis, Fujimori method 1. INTRODUCTION Drowsy driving and not looking ahead carefully is one of the causes of traffic accidents. There are three main ways to prevent from feeling sleepy, to provide awakening stimulus like light and cold water, to intake something with awakening apparatus like caffeine and to take a nap. In these methods, taking a nap is drawing an attention because of can fill sleeping desire immediately. Sleep stages are judged from sleep EEG(electroencephalogram), and its international criteria is proposed by Rechtschaffen and Kales [1]. Generally, men feel dullness after a long nap. It s called as sleep inertia and occurs when you wake up after a deeper than sleep stage 3. Furthermore, there is a problem that it becomes difficult to sleep at night. Therefore, it is recommended that you wake up before entering sleep stage 3. That is, a nap of 2 minutes or less is recommended. In addition, Hayashi et al. proposed that it requires passing through three minutes with sleep stage 2 in order to mitigate sleepiness [2]. Therefore, analysis of sleep stage 2 is required. It is important to find a sleeping spindle as sleep spindle is defined as a feature of sleep stage 2 in international standards. Visual scoring and automatic analysis are orthodox method of EEG analysis. EEG components are classified by frequency band, therefore experts measure period and calculate frequency, and automatic methods use frequency analysis [3 7]. However visual inspection is able to observe chronological changes, there are problems that confirming every EEG component is time consuming and it is required to training to score. Otherwise many automatic analysis methods are proposed and they are based on Fourier and wavelet transformation. In the frequency domain analysis these methods, it is possible to analyze EEG component in a short time. However they couldn t analyze clinically because of they couldn t express change of each waves and it s hard to detect paroxysmal activity. This paper proposes an automatic analysis method which simulates the way of visual scoring. The proposed method expands Fujimori s method in order to improve extraction accuracy of sleep spindle. The sleep spindles are detected using the triangle wave obtained by the improved Fujimori s method and cumulative sum chart (CUSUM chart). Through the design example, we show that the proposed method is superior to the conventional method. 2. PROPOSED METHOD In this section, an automatic sleep spindles extraction method is indicated. The analysis procedure is as follows: 1. EEG preprocessing with low-pass filter 2. Analysis EEG based on the Fujimori s method 3. Binarizing sleep spindle candidates 4. Extraction of sleep spindle candidates using CUSUM chart 5. Detection sleep spindles. Raw EEG-data and processed data are shown in Fig /17/$ IEEE 967 GlobalSIP 217

2 wake[stage W] stage 1 stage 2 stage 3 stage 4 stage REM Table 1. Sleep stage classification α waves are dominant rhythm, sometimes β waves appear. attenutaion (drop out) of the α rhythm and enhanced β activity. appearance of sleep spindles or K-complexes δ activity occupies 2-5 % of the time δ activity represents greater than 5 % of the time A saccadic movement is appear with rapid changes in angular velocity at the onset. wave are decided as follows: EEG [µv] CUSUM chart binary Result of detection 2 1 Fig. 1. analyze method 2.1. EEG preprocessing using low-pass filter Since the EEG includes EMG (Electromyogram) or AC power supply noise, it is necessary to remove them. In order to focus on the sleeping spindle consisting of 11 Hz to 16 Hz, unnecessary components are removed with low-pass FIR digital filter having 5 orders A new analyze EEG based on the Fujimori s method In this paper, a new automatic EEG analysis system based on the Fujimori s method is used. The Fujimori s method is a standard of visual inspection by experts and able to analyze in time domain [8]. It s used when EEG components are superimposed, observe waveform recorded on paper visually. The details of this system are described below [9]. Now, v(k) at point k is an electrical potential, and t k at point k is time on the timing. To analyze EEG based on the Fujimori s method, it needs to detect variation points as maximum points(wave s peak) and minimum points(wave s trough) as shown in Fig. 2. The peak and trough of each 1. if v(k 1) < v(k) and v(k) > v(k + 1), then v(k) is a wave s peak v t and its time is t t. 2. if v(k 1) > v(k) and v(k) < v(k + 1), then v(k) is a wave s trough v b and its time t b. In the conventional Fujimori s method, a wave amplitude as shown in Fig. 2 is defined as vertical distance from a wave s peak to the intersection of line connecting its two wave s troughs. Therefore, the wave amplitude d c is calculated by ( ) vb+1 v b d c = v t (t t t b ) + v b. (1) t b+1 t b Moreover, the wave period T c and the wave frequency f Tc are calculated by T c = t b+1 t b f s (2) and f Tc = 1 T c = f s t b+1 t b, (3) respectively. Here f s is a sampling frequency. Measurement of each wave in a complex waveform is indicated in Fig. 2. That is, it is called as a first-order wave which is independent each wave (the triangle as connected with trough point v e 1, mountain point v p 1 and trough point v e ), second-order wave is superimposed under first-order waves, and third-order wave is superimposed under second-order waves. EEG in a complex waveform is obtained by superimposing of each solo wave. Sleep spindles are between 12 Hz and 14 Hz [1]. Moreover, recent studies are extended this frequency range between 11 Hz and 16 Hz [11 13]. Therefore, because only first-order waves are required to analyze and detect spindles, high frequency component over 16 Hz is removed from EEG with preprosessing low-pass FIR digital filter Binarizing sleep spindle candidates It is said to be enough to observe 5 seconds to confirm appearance of sleep spindles. Therefore, we multiply the rectangular window function, W, to retrieve sleep EEG for 5 seconds as shown in Fig. 3. At this time, starting point of analysis area is 968

3 Fig. 2. EEG analyzation with the Fujimori s method Fig. 3. settings analyzation area with rectangular window W the first trough point of quarried EEG. End point of analysis area is the last trough point of quarried EEG. In order not to lose the continuity of spindle waves, window function is used with 8% overlapped (i.e., shifted by 1 s steps along the EEG record). Now, as shown in Fig.3, it is assumed that there are N troughs between t s and t s+n. That is, the trough is from v s to v s+n. Then, sleep spindle candidates are extracted by Fujimori s method. It is known that a sleep spindle consists of waves from 11 Hz to 16 Hz that occur for at least.5 seconds. In Fujimori s method, the frequency of EEG is given by (2). The amplitude of sleep spindle is defined as the height of the triangle, d c, in (1) in the conventional Fujimori s method. Then, we decided the amplitude of sleep spindle to be over 1µV by examining the waves that clinicians judged as sleep spindle waves [14]. However, the clinician judges the sleep spindle wave by the difference between the peak point and the trough point. Therefore, the amplitude of sleep spindle in the proposed method is redefined as d p = v t v b. (4) d p is shown in Fig. 2. Then, we decide the amplitude of sleep spindle to be over 8µV by examining the waves that clinicians judged as sleep spindle waves. From the above, the candidate of the sleep spindle can be judged by amplitude and frequency. In the proposed method, standards of amplitude and frequency for judging as the sleep spindle are determined as follows: d p 8 (5) and 11 f Tc 16. (6) If the triangle by using Fujimori s method satisfies conditions (5) and (6), then x(k) is set to 1. Otherwise x(k) is set to. Where k is sample point. An example of x(k) is shown in the second row of Fig Extract of sleep spindle using CUSUM In this section, we describe a method for automatically extracting sleep spindle wave using CUSUM chart. The number of samples in the analysis area for CUSUM chart as shown in Fig. 3 is M = f s (t s+n t s ). (7) The cumulative sums c(), c(1),..., c(m) is calculated with c(k) = { (k = ) c(k 1) + (x(k) x) (k = 1, 2,..., M), where x represents average of binarized x(k), given by x = 1 M (8) M x(k). (9) k=1 If there are sleep spindle candidates, cumulative sum c(k) increases as shown in the third row of Fig. 1 or 4. On the other hand, if there are no sleep spindle candidates, cumulative sum c(k) decreases as shown in Fig. 4. Because a sleep spindle consists of waves from 11 Hz to 16 Hz that occur for at least.5 seconds, CUSUM chart must show an increasing trend for more than 5 seconds. Therefore, in this paper, it is judged as sleep spindles when CUSUM chart trends upward for more than.5 seconds. In this case, it matches the visual inspection. However, even if CUSUM chart is not always monotonically increasing as shown in Fig. 4, visual inspection may sometimes judge sleep spindle. Therefore, we perform first order differentiation of c(k) and examine the trend of slope of CUSUM chart in this paper. If adjacent peak points and adjacent trough points for first order differentiation of c(k) increase and the adjacent peak point and trough point are shorter 969

4 Table 2. result of detection percentage conventional Proposed Precision % % Sensitivity % % False Negative rate 8.78 % 2.68 % Fig. 4. detect peak point and trough point from CUSUM chart than.2 seconds, CUSUM chart is determined to be upward trend. That is, it s conditional expression is described as c(p i ) < c(p i+1 ) and c(t i ) < c(t i+1 ) and p i+1 t i <.2sec, (1) where c(p i ) and c(t i ) are ith peak point and ith trough points for first order differentiation of c(k) as shown in Fig. 4, respectively. Spindle candidates are judged as sleep spindles if CUSUM chart is improved longer than.5 second and shorter than 3 second. 3. EVALUATION AND RESULTS The EEG data used in this paper was collected from 7 healthy subjects aged between 21 and 24 years. EEG electrodes located at C3 and A2 with reference to the mastoids according to the 1-2 system. EEG was sampled with a frequency of 5 Hz. In order to evaluate the proposed method, the obtained results compare with visually scored record. Let s consider TP=True Positives, TN=True Negatives, FP=False Positives, and FN=False Negatives. Then, precision, sensitivity and false negative rate are defined as: P recision = T P T P + F P (11) T P Sensitivity = (12) T P + F N F N F alsen egativerate = T P + F N. (13) It analyzes one hour sleep of 7 research subjects. The obtained result of evaluation scores is shown in Table.2. It is clear from the table that sensitivity shows good score, although precision is under 5%. The proposed method of measuring the amplitude using (4) is effective to detect sleep spindles, because sensitivity indicates 91.22% in our previous method which measure amplitude using (1), but too much to find them. Features which couldn t detect in proposed method is these waves not satisfied condition (5). Sleep spindles are concentration of spindle-shaped waves essentially. EEG is different from individual to individual, there are some people whoes EEG frequency is higher or lower as a whole. In this paper, threshold value is set evenly so there are no chance.1 Hz. Therefore, they couldn t detected little difference from the spindle terms. 4. CONCLUSION In this paper, we proposed an enhanced sleep spindle detector in time domain. The amplitude of sleep spindle in the proposed method was expressed by the difference between the peak point and the trough point in the same manner as the visual inspection judgment in order to improve extraction accuracy of sleep spindle. The CUSUM chart was used to connect spindle candidates and improve sensitivity. Through the design example, we showed that the proposed method is superior to the conventional method. The proposed method will be able to analyze not only sleep spindles but also theta and delta waves. 5. REFERENCES [1] Antoine Picot, Harry Whitmore, and Florian Chapotot, Detection of cortical slow waves in the sleep eeg using a modified matching pursuit method with a restricted dictionary, IEEE Transactions on Biomedical Engineering, vol. 59, no. 1, pp , 212. [2] Mitsuo Hayashi, Naoko Motoyoshi, and Tadao Hori, Recuperative power of a short daytime nap with or without stage 2 sleep, Sleep, vol. 28, no. 7, pp , 25. [3] Muammar M Kabir, Reza Tafreshi, Diane B Boivin, and Naim Haddad, Enhanced automated sleep spindle detection algorithm based on synchrosqueezing, Medical & biological engineering & computing, vol. 53, no. 7, pp , 215. [4] João Costa, Manuel Ortigueira, Arnaldo Batista, and T Paiva, An automatic sleep spindle detector based on wt, stft and wmsd, World Academy of Science, Engineering and Technology, International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering, vol. 6, no. 8, pp ,

5 [5] Y-L Hao, Y Ueda, and N Ishii, Improved procedure of complex demodulation and an application to frequency analysis of sleep spindles in eeg, Medical and Biological Engineering and Computing, vol. 3, no. 4, pp , [6] Subin Kuriakose and Geevarghese Titus, Karhunenloeve transform for sleep spindle detection, in Devices, Circuits and Systems (ICDCS), 216 3rd International Conference on. IEEE, 216, pp [14] Yuka Kawashima, Naoyuki Satoh, Takashi Yoshida, Mitsuo Hayashi, Tatsuya Iwaki, and Napyuki Aikawa, An automatic extraction of sleep spindle based on the fujimori method, in Proceedings of 216 AnnualConference of Electronics, Information and Systems Society, I.E.E of Japan, 216, pp [7] Tarek Lajnef, Sahbi Chaibi, Jean-Baptiste Eichenlaub, Perrine M Ruby, Pierre-Emmanuel Aguera, Mounir Samet, Abdennaceur Kachouri, and Karim Jerbi, Sleep spindle and k-complex detection using tunable q-factor wavelet transform and morphological component analysis, Frontiers in human neuroscience, vol. 9, 215. [8] Sunao Uchida, Masato Matsuura, Shigeki Ogata, Takuji Yamamoto, and Naoyuki Aikawa, Computerization of fujimori s method of waveform recognition a review and methodological considerations for its application to allnight sleep eeg, Journal of neuroscience methods, vol. 64, no. 1, pp. 1 12, [9] Bunichi Fujimori, Toshikatsu Yokota, Yasuko Ishibashi, and Tadao Takei, Analysis of the electroencephalogram of children by histogram method, Electroencephalography and Clinical Neurophysiology, vol. 1, no. 2, pp , [1] Allan Rechtschaffen and Anthony Kales, A manual of standardized terminology, techniques, and scoring systems for sleep stages of human subjects, [11] Ankit Parekh, Ivan W Selesnick, David M Rapoport, and Indu Ayappa, Sleep spindle detection using timefrequency sparsity, in Signal Processing in Medicine and Biology Symposium (SPMB), 214 IEEE. IEEE, 214, pp [12] Simon C Warby, Sabrina L Wendt, Peter Welinder, Emil GS Munk, Oscar Carrillo, Helge BD Sorensen, Poul Jennum, Paul E Peppard, Pietro Perona, and Emmanuel Mignot, Sleep-spindle detection: crowdsourcing and evaluating performance of experts, non-experts and automated methods, Nature methods, vol. 11, no. 4, pp , 214. [13] S. Devuyst, T. Dutoit, P. Stenuit, and M. Kerkhofs, Automatic sleep spindles detection ; overview and development of a standard proposal assessment method, in 211 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Aug 211, pp