A Reliable Algorithm Based on Combination of EMG, ECG and EEG Signals for Sleep Apnea Detection : (A Reliable Algorithm for Sleep Apnea Detection)

Moridani, Mohammad Karimi; Heydar, Mahdyar; Behnam, Seyed Sina Jabbari

doi:10.1109/kbei.2019.8734992

Cited by 25 publications

(14 citation statements)

References 33 publications

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“…The discrete wavelet transform of ECG, EMG, and EEG signals were computed in [15] to achieve a time -frequency bands and the transform was fed into a Multilayer Perceptron (MLP). 96.87% Specificity, 97.41% sensitivity, and 98.9% accuracy were obtained through combining EMG, ECG and EEG signals.…”

Section: Literature Reviewmentioning

confidence: 99%

Computer Aided Detection of Obstructive Sleep Apnea from EEG Signals

Bello

Alqasemi

2021

SSRN Journal

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Section: Literature Reviewmentioning

confidence: 99%

Computer Aided Detection of Obstructive Sleep Apnea from EEG Signals

Bello

Alqasemi

2021

SSRN Journal

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“…Polysomnography (PSG) merupakan metode standar bagi tenaga medis untuk merekam aktivitas listrik jantung pada bagian tubuh. PSG umumnya menggunakan lebih dari dua belas sensor [7], di antaranya sensor electrocardiogram (ECG) untuk merekam aktivitas listrik pada bagian jantung [8], [9], sensor electroencephalogram (EEG) untuk merekam aktivitas listrik pada bagian otak [10]- [12], dan sensor yang merekam kandungan SpO2 pada darah [13]. Penggunaan PSG pada dasarnya memiliki beberapa kekurangan, di antaranya kompleksitas dan kerumitan saat perekaman data karena banyaknya sensor yang digunakan.…”

Section: Pendahuluanunclassified

Asesmen ECG-Apnea Satu Sadapan untuk Peningkatan Akurasi Klasifikasi Gangguan Tidur Berdasarkan AdaBoost

Fahruzi

Purnama

Purnomo

2020

JNTETI

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Gangguan tidur adalah terganggunya aliran pernapasan (kolaps) yang berulang saat tidur. Gangguan tidur umumnya tidak terdiagnosis dan tidak tertangani dengan baik sehingga terjadi interupsi berulang pada saluran pernapasan. Kondisi tersebut berpotensi menyebabkan penderita mengalami gangguan tidur yang serius. Salah satu alat bantu yang digunakan untuk menganalisis adanya gangguan tidur pada ritme jantung adalah melalui rekaman electrocardiogram (ECG). ECG merupakan suatu grafik yang merepresentasikan aktivitas listrik pada jantung berupa gelombang P, gelombang QRS, dan gelombang T. Banyaknya sensor untuk merekam ECG menyebabkan ketidaknyamanan pasien saat perekaman data, menyebabkan kompleksitas rekaman, lambatnya komputasi, kesalahan dalam interpretasi, dan kehilangan informasi klinis. Oleh karena itu, dibutuhkan suatu sistem peringatan dini sebagai alat bantu medis yang mampu mendiagnosis rekaman ECG menggunakan satu sadapan. Sistem didesain menjadi lima tahapan, yaitu akuisisi rekaman ECG, praproses, ekstraksi fitur, seleksi fitur, dan proses klasifikasi. Fitur set yang diperoleh dari rekaman ECG berupa variabel-variabel yang diperoleh dari domain waktu, domain frekuensi, dan analisis nonlinear. Pengujian menunjukkan metode AdaBoost memiliki nilai kinerja paling tinggi dibandingkan dengan SVM, k-NN, dan NN. Metode AdaBoost secara berurutan memiliki kinerja classification accuracy, F1 score, precision, recall, sensitivity, dan specificity sebesar 90,1%, 90,1%, 90,3%, 90,1%, 86,9%, dan 93,3%.

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“…2. Mohammad Karimi Moridani et.al [1], using linear and nonlinear analysis in obstructive sleep apnoea (OSA) patients, we diagnosed apnoea events and combined EMG, ECG and EG signals. The linear and nonlinear analysis in the health and apnoea sleep-at-Table environment is performed during different stages of sleep-at-rest and sleepat-rest signals in the OSA patients by the multi-layered Perceptron classifier.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Some sleep disorders can cause fatal or deliberate complications in the social, physical and mental health of a respective person, if the disease is not cured. Few of the most familiar sleeping disorders are sleep apnoea, anxiety syndrome, narcolepsy, tea and sleepwalking and sleep paralysis [1]. Physical, medical, environmental, mental problems, and sleep disorders associated with age-related genetics are common causes of sleep disorders, including nocturnal disorders, drug therapy, and sleep disorders.…”

Section: Introductionmentioning

confidence: 99%

Sleep Apnoea Disorder

Raut¹,

Dixit²

2020

EJERS

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This article provides research on sleep apnoea. Sleep apnoea is a capable for suspending breath or frequently pausing in period of deep sleep. This symptoms may leads to an unappropriate death that makes it a critical sleeping disorder. Periods of apnoea generally lasts for five seconds or hardly a minute which affects the sleeping pattern due to breathing. This probably happens five times of an hour or even more. Obstructive sleep apnoea (OSA),central sleep apnoea (CSA) and mixed/complex sleep apnoea(MSA) are common three types of apnoea, where mixed/complex sleep apnoea is combination of other two apnoea. Airway obstruction is caused in OSA, while in CSA airway is not blocked, but the brain dosn’t sends proper signals to the muscles that cause instability of the respiratory center. The study includes the sleep disorders, types, cause, signs and symptoms and methods of Sleep Apnoea. Considering the study; it is very much required to detection of sleep apnoea using noninvasive techniques. Machine learning algorithms based detection of sleep apnoea is a feasible solution which provides more than 90% accuracy. The study surveys the similar techniques based on machine learning.

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A Reliable Algorithm Based on Combination of EMG, ECG and EEG Signals for Sleep Apnea Detection : (A Reliable Algorithm for Sleep Apnea Detection)

Cited by 25 publications

References 33 publications

Computer Aided Detection of Obstructive Sleep Apnea from EEG Signals

Computer Aided Detection of Obstructive Sleep Apnea from EEG Signals

Asesmen ECG-Apnea Satu Sadapan untuk Peningkatan Akurasi Klasifikasi Gangguan Tidur Berdasarkan AdaBoost

Sleep Apnoea Disorder

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