{"id":6854,"date":"2016-04-28T08:15:20","date_gmt":"2016-04-28T08:15:20","guid":{"rendered":"http:\/\/biomedpharmajournal.org\/?p=6854"},"modified":"2020-04-24T06:54:47","modified_gmt":"2020-04-24T06:54:47","slug":"detection-of-sleep-disorder-breathing-sdb-using-short-time-frequency-analysis-of-psd-approach-applied-on-eeg-signal","status":"publish","type":"post","link":"https:\/\/biomedpharmajournal.org\/staging\/vol9no1\/detection-of-sleep-disorder-breathing-sdb-using-short-time-frequency-analysis-of-psd-approach-applied-on-eeg-signal\/","title":{"rendered":"Detection of Sleep Disorder Breathing (SDB) Using Short Time Frequency Analysis of PSD Approach Applied on EEG Signal"},"content":{"rendered":"<p><strong>Introduction<\/strong><\/p>\n<p>Sleep apnea is a disorder of disrupted breathing during sleep. It again and again appears in corporation with fat enlargement or loss of tissue tone with collapse. These differences allowance the windpipe to crash at the time of breathing when tissues calm down all along sleep. This complication is called obstructive sleep apnea, is normally correlated with loud snuffling (though not everybody who snores has this disorder). Sleep apnea can also appear if the neurons that govern breathing malfunction at the time of sleep. During an incident of obstructive apnea, the person&#8217;s attempt to inhale air generates indigestion which crashes the windpipe. This blocks the air discharge for few seconds to a minute although the sleeping person struggles to inhale air. When somebody\u2019s blood oxygen level decline, the brain responds by arousal the person enough to narrow the upper airway tissues and open the windpipe. The person may grunt or blow, then restart sniffling [1]. This period may be imitated hundreds of times a night. The continual awaking that sleep apnea patients experience leave them continuously sleepy and may lead to change in nature such as annoyance or abasement. Sleep apnea also deprives the person of oxygen, which can lead to morning inconvenience, a loss of concern in sex, or a failure in mental working. It may lead to high blood pressure, uneven heartbeats, and an expanded hazard of heart attacks and stroke. Patients with serious, untreated sleep apnea disorder are two to three times more feasible to have automobile calamities than the general population. In some high-risk persons, sleep apnea may even lead to quick death from gasping arrest at the time of sleep. An approximated 18 million Americans have sleep apnea [2][3].<\/p>\n<p>Nonetheless, small members have had the problem analyzed. Patients with the exemplary appearances of sleep apnea, such as loud snorting, fatness, and inordinate daytime drowsiness, should be point out to a particular sleep center that can implement a test called Polysomnography. This test reports the patient&#8217;s brain movement, heartbeat, and inhaling during whole night. If sleep apnea is determined, a few treatments are accessible. Moderate sleep apnea usually can be overthrown through weight loss or by prohibiting the person from getting asleep on his or her back. Other people may demand exclusive devices or surgery to correct the impediment. People with sleep apnea should never take prescription or sleeping pills, which can influence their breathing throughout awaking [4].<\/p>\n<p><strong>Symptoms Of Sleep Apnea<\/strong><\/p>\n<p>The signs and symptoms of central and obstructive sleep apneas overlap, consistently making the type of sleep apnea more crucial to determine. The most recurrent signs and symptoms of disruptive and central sleep apneas include:<\/p>\n<p>Drowsiness (hypersomnia) noisy snoring which is typically more important in obstreperous sleep apnea<\/p>\n<p>Episodes of breathing cessation during sleep witnessed by another person.<\/p>\n<p>Abrupt awakening accompanied by shortness of breath, which more likely indicates central sleep apnea<\/p>\n<p>Awakening with a dry mouth or sore throat, Morning headache, Difficulty staying asleep (insomnia), Attention problems, unnecessary daytime sleepiness, walking with an unusual feeling after sleep, Having problems with reminiscence and attentiveness, Experiencing behavior changes, Morning or night headaches, About half of all people with sleep apnea report headaches, Heartburn or a sour taste in the mouth at night, Swelling of the legs, Sweating and chest pain while you are sleeping, Loud snoring. Almost all people who have sleep apnea snore, Restless tossing and turning during sleep, Mouth breathing, receiving up often during the night to urinate, impatience, Waking up a lot, Bed wetting and Not growing as rapidly as they should for their time. This may be the only indication in some kids [2]\n<p><strong>Analysis Of Eeg Signals<\/strong><\/p>\n<p>Different signals considered by biomedical signals. All signals are obtained by performing signal data extraction by performing signal data extraction of data file named as n2_edfm.mat and sdb1_edfm.mat.<\/p>\n<p><strong>\u00a0<\/strong><strong>Normal And Patient Detail<\/strong><\/p>\n<p>Sixteen volunteer subjects were chosen for this study. These were referred to as the normal group as they were recorded to have no cardiac or respiratory complications and also any sleep related problem. Another group has sleep disorder of SDB of four subjects.<\/p>\n<p><strong>Load The Eeg Data<\/strong><\/p>\n<p>Figure 1 shows a plot of EEG signal of a sdb subject SDB1 with data base name \u2018sdb1_edfm.mat\u2019 it shows the ROCLOC channel it is \u00a0channel of the given data[5].<\/p>\n<table style=\"width: 70%;\" border=\"1\" cellpadding=\"5\">\n<tbody>\n<tr>\n<td><img decoding=\"async\" class=\"alignnone size-thumbnail wp-image-6855\" src=\"https:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig1-150x150.jpg\" alt=\"Figure 1: EEG signal of So stage (channel ROCLOC) of subject sdb1\" width=\"150\" height=\"150\" srcset=\"https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig1-150x150.jpg 150w, https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig1-256x256.jpg 256w, https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig1.jpg 567w\" sizes=\"(max-width: 150px) 100vw, 150px\" \/><\/td>\n<td><strong>Figure 1: EEG signal of So stage (channel ROCLOC) of subject sdb1<\/strong><\/p>\n<p><a href=\"http:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig1.jpg\" target=\"_blank\">Click here to View figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p><strong>Extraction Of Channel\u00a0\u00a0\u00a0\u00a0 <\/strong><\/p>\n<p>In figure 2 we have shown the extracted signal of duration of 1min (60 sec) consisting of EEG signal of the respective channel for sleep stage So for subject sdb1. Here the total signal is of 1min, sampling frequency 256 Hz (for subject sdb1) [6] [7] .<\/p>\n<table style=\"width: 70%;\" border=\"1\" cellpadding=\"5\">\n<tbody>\n<tr>\n<td><img decoding=\"async\" class=\"alignnone wp-image-6856 size-thumbnail\" src=\"https:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig2-150x150.jpg\" alt=\"Fig 2 Extraction of channel ROCLOC of So stage of subject sdb1\" width=\"150\" height=\"150\" srcset=\"https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig2-150x150.jpg 150w, https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig2-256x256.jpg 256w, https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig2.jpg 546w\" sizes=\"(max-width: 150px) 100vw, 150px\" \/><\/td>\n<td><strong>Figure 2: Extraction of channel ROCLOC of So stage of subject sdb1<\/strong><\/p>\n<p><a href=\"http:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig2.jpg\" target=\"_blank\">Click here to View figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p>In figure 3 we have shown the clipped signal of duration of 1min (60 sec) consisting of EEG signal of the respective channel for sleep stage So for subject sdb1. Here the total signal is of 1min, sampling frequency 256 Hz (for subject sdb1) respectively.<\/p>\n<table style=\"width: 70%;\" border=\"1\" cellpadding=\"5\">\n<tbody>\n<tr>\n<td><img decoding=\"async\" class=\"alignnone size-thumbnail wp-image-6857\" src=\"https:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig3-150x150.jpg\" alt=\"Figure 3: Extraction of signal on the basis of frequency (stage So) of channel ROCLOC, subject sdb1\" width=\"150\" height=\"150\" srcset=\"https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig3-150x150.jpg 150w, https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig3.jpg 625w\" sizes=\"(max-width: 150px) 100vw, 150px\" \/><\/td>\n<td><strong>Figure 3: Extraction of signal on the basis of frequency (stage So) of channel ROCLOC, subject sdb1<\/strong><\/p>\n<p><a href=\"http:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig3.jpg\" target=\"_blank\">Click here to View figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>In figure 4 we have shown the clipped signal of duration of 1min (60 sec) consisting of EEG signal of the respective channel for sleep stage S1 for subject sdb1. Here the total signal is of 1min, sampling frequency 256 Hz (for subject sdb1).<\/p>\n<table style=\"width: 70%;\" border=\"1\" cellpadding=\"5\">\n<tbody>\n<tr>\n<td><img decoding=\"async\" class=\"alignnone size-thumbnail wp-image-6858\" src=\"https:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig4-150x150.jpg\" alt=\"Figure 4: Extraction of signal on the basis of frequency (stage S1) of channel ROCLOC, subject sdb1\" width=\"150\" height=\"150\" srcset=\"https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig4-150x150.jpg 150w, https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig4-256x256.jpg 256w, https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig4.jpg 631w\" sizes=\"(max-width: 150px) 100vw, 150px\" \/><\/td>\n<td><strong>Figure 4: Extraction of signal on the basis of frequency (stage S1) of channel ROCLOC, subject sdb1 <\/strong><\/p>\n<p><a href=\"http:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig4.jpg\" target=\"_blank\">Click here to View figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>In figure 5 we have shown the clipped signal of duration of 1min (60 sec) consisting of EEG signal of the respective channel for sleep stage S2 for subject sdb1. Here the total signal is of 1min, sampling frequency 256 Hz (for subject sdb1).<\/p>\n<table style=\"width: 70%;\" border=\"1\" cellpadding=\"5\">\n<tbody>\n<tr>\n<td><img decoding=\"async\" class=\"alignnone size-thumbnail wp-image-6859\" src=\"https:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig5-150x150.jpg\" alt=\"Figure 5: Extraction of signal on the basis of frequency (stage S2) of channel ROCLOC, subject sdb1\" width=\"150\" height=\"150\" srcset=\"https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig5-150x150.jpg 150w, https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig5-256x256.jpg 256w, https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig5.jpg 627w\" sizes=\"(max-width: 150px) 100vw, 150px\" \/><\/td>\n<td><strong>Figure 5: Extraction of signal on the basis of frequency (stage S2) of channel ROCLOC, subject sdb1 <\/strong><\/p>\n<p><a href=\"http:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig5.jpg\" target=\"_blank\">Click here to View figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>In figure 6 we have shown the clipped signal of duration of 1min (60 sec) consisting of EEG signal of the respective channel for sleep stage S3 for subject sdb1. Here the total signal is of 1min, sampling frequency 256 Hz (for subject sdb1)<\/p>\n<table style=\"width: 70%;\" border=\"1\" cellpadding=\"5\">\n<tbody>\n<tr>\n<td><a href=\"http:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig6.jpg\"><img decoding=\"async\" class=\"alignnone size-thumbnail wp-image-6862\" src=\"https:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig6-150x150.jpg\" alt=\"Figure 6: Extraction of signal on the basis of frequency (stage S3) of channel ROCLOC, subject sdb1\" width=\"150\" height=\"150\" srcset=\"https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig6-150x150.jpg 150w, https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig6-256x256.jpg 256w, https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig6.jpg 631w\" sizes=\"(max-width: 150px) 100vw, 150px\" \/><\/a><\/td>\n<td><strong>Figure 6: Extraction of signal on the basis of frequency (stage S3) of channel ROCLOC, subject sdb1<\/strong><\/p>\n<p><a href=\"http:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig6.jpg\" target=\"_blank\">Click here to View figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>In figure 7 we have shown the clipped signal of duration of 1min (60 sec) consisting of EEG signal of the respective channel for sleep stage S4 for subject sdb1. Here the total signal is of 1min, sampling frequency 256 Hz (for subject sdb1.<\/p>\n<table style=\"width: 70%;\" border=\"1\" cellpadding=\"5\">\n<tbody>\n<tr>\n<td><img decoding=\"async\" class=\"alignnone size-thumbnail wp-image-6861\" src=\"https:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig7-150x150.jpg\" alt=\"Figure 7: Extraction of signal on the basis of frequency (stage S4)of channel ROCLOC, subject sdb1\" width=\"150\" height=\"150\" srcset=\"https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig7-150x150.jpg 150w, https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig7-256x256.jpg 256w, https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig7.jpg 623w\" sizes=\"(max-width: 150px) 100vw, 150px\" \/><\/td>\n<td><strong>Figure 7: Extraction of signal on the basis of frequency (stage S4)of channel ROCLOC, subject sdb1 <\/strong><\/p>\n<p><a href=\"http:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig7.jpg\" target=\"_blank\">Click here to View figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>In figure 8 we have shown the clipped signal of duration of 1min (60 sec) consisting of EEG signal of the respective channel for sleep stage REM for subject sdb1. Here the total signal is of 1min, sampling frequency 256 Hz (for subject sdb1) .<\/p>\n<table style=\"width: 70%;\" border=\"1\" cellpadding=\"5\">\n<tbody>\n<tr>\n<td><img decoding=\"async\" class=\"alignnone size-thumbnail wp-image-6863\" src=\"https:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig8-150x150.jpg\" alt=\"Figure 8 Extraction of signal on the basis of frequency (stage REM) of channel ROCLOC, subject sdb1\" width=\"150\" height=\"150\" srcset=\"https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig8-150x150.jpg 150w, https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig8-256x256.jpg 256w, https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig8.jpg 625w\" sizes=\"(max-width: 150px) 100vw, 150px\" \/><\/td>\n<td><strong>Figure 8 Extraction of signal on the basis of frequency (stage REM) of channel ROCLOC, subject sdb1<\/strong><\/p>\n<p><a href=\"http:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig8.jpg\" target=\"_blank\">Click here to View figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><strong>Filtering Of The Signal<\/strong><\/p>\n<p>The clipped signal are passed through the Hanning window low pass filter for removing the high frequency components that eventually indicates the noise because major portion of EEG signals are limited within the range of 25Hz. Figure 9 shows the filtering of the signal for\u00a0 sdb1[6][8].<\/p>\n<table style=\"width: 70%;\" border=\"1\" cellpadding=\"5\">\n<tbody>\n<tr>\n<td><img decoding=\"async\" class=\"alignnone size-thumbnail wp-image-6864\" src=\"https:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig9-150x150.jpg\" alt=\"Figure 9: Extracted signal is filtered (duration is 60 sec), of subject sdb1.\" width=\"150\" height=\"150\" srcset=\"https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig9-150x150.jpg 150w, https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig9.jpg 612w\" sizes=\"(max-width: 150px) 100vw, 150px\" \/><\/td>\n<td><strong>Figure 9: Extracted signal is filtered (duration is 60 sec), of subject sdb1.<\/strong><\/p>\n<p><a href=\"http:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig9.jpg\" target=\"_blank\">Click here to View figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><strong>Comparison Between Filtered And Non-Filtered Signal<\/strong><\/p>\n<p>Now comparison of each clipped signal is shown. Figure 10 shows the comparison between the filtered and non-filtered signal for sdb1 (stage So).<\/p>\n<table style=\"width: 70%;\" border=\"1\" cellpadding=\"5\">\n<tbody>\n<tr>\n<td><img decoding=\"async\" class=\"alignnone size-thumbnail wp-image-6865\" src=\"https:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig10-150x150.jpg\" alt=\"Figure 10: Comparison between filtered and non-filtered signal, of subject sdb1\" width=\"150\" height=\"150\" srcset=\"https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig10-150x150.jpg 150w, https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig10.jpg 624w\" sizes=\"(max-width: 150px) 100vw, 150px\" \/><\/td>\n<td>\u00a0<strong>Figure 10: Comparison between filtered and non-filtered signal, of subject sdb1 <\/strong><\/p>\n<p><a href=\"http:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig10.jpg\" target=\"_blank\">Click here to View figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p><strong>Psd Estimation<\/strong><\/p>\n<p>After applying the filtering each signal is passed through the sliding window to measure the power spectrum density (PSD). Since PSD gives signal power with respect to frequency spectrum, we require specifying the number of frequency slots to distribute the spectral power.In figure 11 we have shown the PSD estimation curve of subject Sdb1 for So stage[6][9].<\/p>\n<table style=\"width: 70%;\" border=\"1\" cellpadding=\"5\">\n<tbody>\n<tr>\n<td><img decoding=\"async\" class=\"alignnone size-thumbnail wp-image-6866\" src=\"https:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig11-150x150.jpg\" alt=\"Figure 11: PSD estimation curve (stage So) of channel ROC-LOC for subject sdb1\" width=\"150\" height=\"150\" srcset=\"https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig11-150x150.jpg 150w, https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig11-256x256.jpg 256w, https:\/\/biomedpharmajournal.org\/staging\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig11.jpg 559w\" sizes=\"(max-width: 150px) 100vw, 150px\" \/><\/td>\n<td><strong>Figure 11: \u00a0PSD estimation curve (stage So) of channel ROC-LOC for \u00a0subject sdb1<\/strong><\/p>\n<p><a href=\"http:\/\/biomedpharmajournal.org\/wp-content\/uploads\/2016\/04\/Vol9_No1_dete_mohd_fig11.jpg\" target=\"_blank\">Click here to View figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p><strong>Result\u00a0<\/strong><\/p>\n<p>Normalized power (P norm) of normal cases having no indication of sleep is figure out and correlated with pathological cases all along different sleep stages. Normalized power shows the percentage of a specific EEG activity out of complete power. So it is found a better explanation of assessment of detection of features in reverse of taking average power of peculiar EEG activity [6][10].<\/p>\n<p>For delta activity normalized power for normal cases is found in range of 0.75 \u2014 0.81 same calculation for cases under SDB disorder is found to be 0.82 \u2014 0.83 i.e. normalized power for delta activity during So stage for SDB disorder is quite high in comparison to normal case.(see table 1).<\/p>\n<p><strong>Table 1: Comparison of normal and SDB subject for delta activity<\/strong><\/p>\n<table style=\"width: 95%;\" border=\"1\" cellspacing=\"0\" cellpadding=\"4\">\n<tbody>\n<tr>\n<td style=\"text-align: center;\" width=\"157\"><strong>NORMAL\/PATIENT<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"77\"><strong>n1<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"45\"><strong>n2<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"56\"><strong>n3<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"56\"><strong>n11<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"56\"><strong>sdb1<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"84\"><strong>sdb2<\/strong><\/td>\n<\/tr>\n<tr>\n<td width=\"157\">P_delta<\/td>\n<td width=\"77\">0.58<\/td>\n<td width=\"45\">0.76<\/td>\n<td width=\"56\">0.75<\/td>\n<td width=\"56\">0.78<\/td>\n<td width=\"56\">0.82<\/td>\n<td width=\"84\">0.83<\/td>\n<\/tr>\n<tr>\n<td width=\"157\"><\/td>\n<td colspan=\"4\" width=\"234\">LOW<\/td>\n<td colspan=\"2\" width=\"141\">HIGH<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>For theta activity normalized power for normal cases is found in range of 0.16 \u2014 0.28 same calculation for cases under SDB disorder is found to be 0.10 \u2014 0.14 i.e. normalized power for theta activity during So stage for SDB disorder is quite\u00a0 low in comparison to normal cases.(see table 2)<\/p>\n<p><strong>Table 2: Comparison of normal and SDB subject for theta activity<\/strong><\/p>\n<table style=\"width: 95%;\" border=\"1\" cellspacing=\"0\" cellpadding=\"4\">\n<tbody>\n<tr>\n<td style=\"text-align: center;\" width=\"150\"><strong>NORMAL\/PATIENT<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"46\"><strong>n1<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"46\"><strong>n2<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"46\"><strong>n3<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"46\"><strong>n5<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"46\"><strong>n11<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"47\"><strong>sdb1<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"47\"><strong>sdb2<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"47\"><strong>sdb4<\/strong><\/td>\n<\/tr>\n<tr>\n<td width=\"150\">P_theta<\/td>\n<td width=\"46\">0.28<\/td>\n<td width=\"46\">0.17<\/td>\n<td width=\"46\">0.19<\/td>\n<td width=\"46\">0.16<\/td>\n<td width=\"46\">0.18<\/td>\n<td width=\"47\">0.12<\/td>\n<td width=\"47\">0.14<\/td>\n<td width=\"47\">0.10<\/td>\n<\/tr>\n<tr>\n<td width=\"150\"><\/td>\n<td colspan=\"5\" width=\"229\">HIGH<\/td>\n<td colspan=\"3\" width=\"141\">LOW<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>For alpha activity normalized power for normal cases is found in range of 0.01 \u2014 0.12 same calculation for cases under SDB disorder is found to be 0.06 \u2014 0.16 i.e. normalized power for alpha activity during So stage for SDB disorder is quite high in comparison to normal cases.(see table 3)<\/p>\n<p><strong>Table 3: Comparison of normal and SDB subject for alpha activity<\/strong><\/p>\n<table style=\"width: 95%;\" border=\"1\" cellspacing=\"0\" cellpadding=\"4\">\n<tbody>\n<tr>\n<td style=\"text-align: center;\" colspan=\"2\" width=\"133\"><strong>NORMAL\/PATIENT<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"87\"><strong>n1<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"87\"><strong>n2<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"89\"><strong>sdb2<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"89\"><strong>sdb3<\/strong><\/td>\n<td style=\"text-align: center;\" width=\"89\"><strong>sdb4<\/strong><\/td>\n<\/tr>\n<tr>\n<td colspan=\"2\" width=\"133\">P_alpha<\/td>\n<td width=\"87\">0.10<\/td>\n<td width=\"87\">0.05<\/td>\n<td width=\"89\">0.16<\/td>\n<td width=\"89\">0.11<\/td>\n<td width=\"89\">0.06<\/td>\n<\/tr>\n<tr>\n<td width=\"132\"><\/td>\n<td colspan=\"3\" width=\"175\">LOW<\/td>\n<td colspan=\"3\" width=\"267\">HIGH<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><strong>Conclusion <\/strong><\/p>\n<p>It was shown, that the time profiles of different patients and normal patients reflect the structure of EEG signal during quiet and active sleep. The procedure is capable of working even in the presence of different PSD estimation methods. A comprehensive comparison between the ROC-LOC channel, EEG sourced, PSD of affected and non-affected individuals has been made. The results in the tested channels indicate a significant difference in amplitudes between the control group and reference group.<\/p>\n<p>As the above statistics indicate, the approach utilized over the course of this study enables the identification of Sleep Disorder Breathing patients from healthy individuals in a relatively easier manner as compared to most contemporary techniques in vogue. Further work on this technique will aim at establishing concrete boundaries of PSD for patients and non-affected individuals, for wide-scale diagnostic applications.<\/p>\n<p><strong>Abbreviations<\/strong><\/p>\n<p>EEG: Electro Encephalogram; ROC-LOC: Right of Central &amp; Left of Central; PSD: Power spectrum Density<\/p>\n<p><strong>Competing interests<\/strong><\/p>\n<p>MMS is researcher. The other authors declare that they have no competing interests.<\/p>\n<p><strong>Authors\u2019 Contributions<\/strong><\/p>\n<p>MMS designed the algorithm for experiments&amp; performed the experiments and analyzed the data. AA edited my Manuscript. GS&amp; SHS help me and guide me during my work. All authors read and approved the final manuscript.<\/p>\n<p><strong>References<\/strong><\/p>\n<ol>\n<li>V\u00e1rady, P\u00e9ter, et al. &#8220;A novel method for the detection of apnea and hypopnea events in respiration signals.&#8221;\u00a0Biomedical Engineering, IEEE Transactions on49.9 (2002): 936-942.<\/li>\n<li>McNames, J. N., and A. M. Fraser. &#8220;Obstructive sleep apnea classification based on spectrogram patterns in the electrocardiogram.&#8221;\u00a0Computers in Cardiology 2000. IEEE, 2000.<\/li>\n<li>Watanabe, Takashi, and Kajiro Watanabe. &#8220;Noncontact method for sleep stage estimation.&#8221;\u00a0Biomedical Engineering, IEEE Transactions on\u00a051.10 (2004): 1735-1748.<\/li>\n<li>Siddiqui, Mohd Maroof, et al. &#8220;EEG Signals Play Major Role to diagnose Sleep Disorder.&#8221; International Journal of Electronics and Computer Science Engineering (IJECSE) 2.2 (2013): 503-505.http:\/\/physionet.org\/cgi-bin\/atm\/ATM<\/li>\n<li>Siddiqui, Mohd Maroof, et al. &#8220;Detection of rapid eye movement behavior disorder using short time frequency analysis of PSD approach applied on EEG signal (ROC-LOC).&#8221;Biomedical Research 26.3 (2015): 587- 593.<\/li>\n<li>Krajca V, Petranek S, Paul K , Matousek M, Mohylova J, and Lhotska L, \u201cAutomatic Detection of Sleep Stages in Neonatal EEG Using the Structural Time Profiles\u201d, Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, September 1-4, 2005<\/li>\n<li>Acharya RU, Faust O, Kannathal N, Chua T, Laxminarayan S, \u201cNon-linear analysis of EEG signals at various sleep stages\u201d Elsevier, Computer Methods and Programs in Biomedicine 2005; 80: 37-45.<\/li>\n<li>Liu D, Pang Z, Lloyd SR, \u201cNeural Network Method for Detection of Obstructive Sleep Apnea and Narcolepsy Based on Pupil Size and EEG\u201d, IEEE Transactions on Neural Networks, Vol. 19, No. 2, (February 2008), pp. 308-318, ISSN 1045-9227.<\/li>\n<li>Iasemidis LD, \u201cEpileptic seizure prediction and control\u201d, IEEE Trans.Biomed. Engng., 50, 2003, 549\u2013558.<\/li>\n<li>Siddiqui, Mohd Maroof, et al. &#8220;Detection of Periodic Limb Movement with the Help of Short Time Frequency Analysis of PSD Applied on EEG Signals.&#8221;\u00a0Extraction\u00a04.11 (2015).<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"<p>Introduction Sleep apnea is a disorder of disrupted breathing during  [&#8230;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[30],"tags":[],"class_list":["post-6854","post","type-post","status-publish","format-standard","hentry","category-vol9no1"],"_links":{"self":[{"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/posts\/6854","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/comments?post=6854"}],"version-history":[{"count":5,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/posts\/6854\/revisions"}],"predecessor-version":[{"id":32641,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/posts\/6854\/revisions\/32641"}],"wp:attachment":[{"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/media?parent=6854"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/categories?post=6854"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/tags?post=6854"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}