1900 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 54, NO. 10, OCTOBER 2007 in the literature: In [2], 69%; in [4], 54.79%; in [5], 95.1%; in [10], 62.33%; in [12], 91%; in [13], 67.2%; in [14], 84%; in [18], 86.89% were 13.11%, 4.92%, 4.92%, 6.56%, 8.20%, and 13.11% for groups 1 to 6, respectively. In assessing the subjects belonging to the 7th group, the system gave some incorrect classifications, patients from postoper- atory were identified as belonging to the chronic laryngitis because of the extreme similarity of voice quality. Due to the high average , causal by the small number of subjects, it is interesting that future works use bigger databases. Other suggestions are: assessing different cost functions in BBA and/or the use of radial base function ANN. All in all, it is important to remark that this system is able to classify the probability of some subject having a specific disease in physicians’ of- fice, using just a simple set of hardware/software. This approach can be very useful to help diagnosis. REFERENCES [1] R. J. Baken and R. F. Orlikoff, Clinical Measurement of Speech and Voice, 2nd ed. San Diego, CA: Singular Thomsom Learning, 2000. [2] D. G. Childers and K. S. Bae, “Detection of laryngeal function using speech and electroglottographic data,” IEEE Trans. Biomed. Eng., vol. 39, no. 1, pp. 19–25, Jan. 1992. [3] B. Fritzell, “Inverse filtering,” J. Voice, vol. 6, no. 2, 1992. [4] M. O. Rosa, M. J. C. Pereira, and M. Grellet, “Adaptive estimation of residue signal for voice pathology diagnosis,” IEEE Trans. Biomed. Eng., vol. 47, no. 1, pp. 96–104, Jan. 2000. [5] S. Hadjitodorov, B. Boyanov, and B. Teston, “Laryngeal pathology detection by means of class-specific neural maps,” IEEE Trans. Inf. Technol. Biomed., vol. 4, no. 1, pp. 68–73, Mar. 2000. [6] C. E. Martinez and H. L. Rufiner, “Acoustic analysis of speech for detection of laryngeal pathologies,” in Proc. IEEE-EMBS Int. Conf. Inf. Tech. Appl. Biomed., 2000, pp. 2369–2372. [7] R. T. Ritchings, M. Mcguillion, and C. J. Moore, “Pathological voice quality assessment using artificial neural networks,” presented at the 2nd Int. Workshop Models and Analysis of Vocal Emissions for Biomedical Application, Firenze, Italy, 2001. [8] M. Frohlich, D. Michaelis, and S. H. Werner, “Acoustic ’breathiness measures’ in the description of pathologic voices,” in Proc. 1998 IEEE Int. Conf. Acoustics Speech and Signal Processing., vol. 2, pp. 937–940. [9] M. O. Rosa, M. Greller, and A. Carvalho, “Signal processing and sta- tistical procedures to identify laryngeal pathologies,” in 6th Int. Conf. IEEE Circuits and Systems Society Electronics, Circuits and Systems., 1999. [10] M. O. Rosa, C. J. Pereira, and A. Carvalho, “Evaluation of neural classi- fiers using statistic methods for identification of laryngeal pathologies,” in Proc. 5th Brazilian Symp. Neural Networks, Brazil, Dec. 9–10, 1998, pp. 220–225. [11] G. V. D. Wouwer, P. Scheunders, and D. V. Dyck, “Wavelet-Filvq Classifier for Speech Analysis,” in Proc. 13th Int. Conf. Pattern Recog- nition (ICPR’96), Antwerp, Belgium, 1997, vol. 4, p. 214. [12] K. Umapathy et al., “Discrimination of pathological voices using a time-frequency approach,” IEEE Trans. Biomed. Eng., vol. 52, no. 3, pp. 421–421, Mar. 2005. [13] A. Parraga, M. A. Zaro, and A. Schuck Jr., “Quantitative assessment of the use of continuous wavelet transform in the analysis of the funda- mental frequency disturbance of the synthetic voice,” Med. Eng. Phys. PII: S, vol. 2, no. 00050-4, pp. 1350–4533. [14] A. Schuck, Jr., and A. Parraga, “On the use of the wavelet packet trans- form as a feature extractor for pathological voice assessement,” pre- sented at the IFMBE Proceedings EMBEC’02 2nd Eur. Medical and Biological Engineering Conf., Vienna, Austria, 2002. [15] A. Schuck, Jr, L. V. Guimarães, and O. Wisbeck, “Dysphonic voice classification using wavelets packets transform and artificial neural net- works,” in Proc. 25th Annu. Int. Conf. IEEE-EMBS, Cancún, Mexico, 2003, pp. 2958–2961. [16] S. Haykin, Neural Network: A Comprehensive Foundation, 2nd ed. Upper Saddle River, NJ: Prentice-Hall, 1998. [17] M. V. Wickerhauser, Adapted Wavelet Packet Analysis From Theory to Software. Wellesley, MA: AK Peters, Ltd., 1994. [18] C. D. P. Crovato and A. Schuck, Jr, “Classificação de sinais de voz utilizando a transformada wavelet packet e redes neurais artificiais,” in Proc. III Congresso Latinoamericano De Engenharia Biomedica, João Pessoa, Paraíba, Brazil, 2004, vol. 5, pp. 1027–1030, ISBN: 85-98739-01-04. Use of Sample Entropy Approach to Study Heart Rate Variability in Obstructive Sleep Apnea Syndrome Haitham M. Al-Angari* and Alan V. Sahakian Abstract—Sample entropy, a nonlinear signal processing approach, was used as a measure of signal complexity to evaluate the cyclic behavior of heart rate variability (HRV) in obstructive sleep apnea syndrome (OSAS). In a group of 10 normal and 25 OSA subjects, the sample entropy measure showed that normal subjects have significantly more complex HRV pattern than the OSA subjects ( ). When compared with spectral analysis in a minute-by-minute classification, sample entropy had an accuracy of 70.3% (69.5% sensitivity, 70.8% specificity) while the spectral analysis had an accuracy of 70.4% (71.3% sensitivity, 69.9% specificity). The combination of the two methods improved the accuracy to 72.9% (72.2% sensitivity, 73.3% specificity). The sample entropy approach does not show major improvement over the existing methods. In fact, its accuracy in detecting sleep apnea is relatively low in the well classified data of the physionet. Its main achievement however, is the simplicity of computation. Sample entropy and other nonlinear methods might be useful tools to detect apnea episodes during sleep. Index Terms—Approximate entropy, heart rate variability, nonlinear signal processing, obstructive sleep apnea, power spectral density, sample entropy. I. INTRODUCTION Heart rate variability (HRV) varies from wakefulness to sleep due to normal changes in the autonomic system activities. Sympathetic tone drops from wakefulness over nonrapid eye movement (NREM) sleep stages, while it shows an increase in REM sleep [1]. Parasympa- thetic activity increases from wakefulness over NREM sleep [2]. Spec- tral analysis of HRV is used to evaluate the activity of the autonomic nervous system. Low-frequency (LF) components (0.04–0.15 Hz) eval- uate the sympathovagal balance while high-frequency (HF) compo- nents (0.15–0.4 Hz) estimate the parasympathetic tone related to res- piratory rhythm [3]. In sleep disorders, impairment of the autonomic nervous system is observed. Studies of muscle sympathetic nerve ac- tivity (MSNA) have shown an increase in sympathetic tone in patients with obstructive sleep apnea syndrome (OSAS) during sleep and wake- fulness [4], [5]. These findings were supported by results from spec- tral analysis. The HF power was significantly diminished and LF/HF ratio was enhanced in awake OSA patients, which indicates a drop in the parasympathetic tone associated with an increase in the sympa- thetic tone [6]. At the start of the apnea episode however, RR inter- vals lengthen which indicates an increase in the vagal activation [7]. There is also a noticeable increase in MSNA, peaking immediately prior to apnea cessation. Arousal at the termination of an apnea ini- tiates a burst of sympathetic activity (associated with an increase in blood pressure and heart rate). This is observed as cyclical variation (progressive bradycardia followed by abrupt tachycardia) of the heart rate [8]. Nonlinear analysis of time series provides a parameter set that quan- tifies the characteristics of the system attractor even when the model Manuscript received January 2, 2006; revised October 29, 2006. Asterisk in- dicates corresponding author. *H. M. Al-Angari was with Northwestern University, Evanston, IL 60208 USA. He is now with the Electrical and Computer Engineering Department, King AbdulAziz University, P O Box 80204, Jeddah, 21589 Saudi Arabia (e-mail: hangari@kau.edu.sa). A. V. Sahakian is with the Electrical Engineering and Computer Science De- partment, Evanston, IL 60208 USA (e-mail: sahakian@ ece.northwestern.edu). Digital Object Identifier 10.1109/TBME.2006.889772 0018-9294/$25.00 © 2007 IEEE