Deep Convolutional Neural Networks and Learning ECG Features for Screening Paroxysmal Atrial Fibrillation Patients

Pourbabaee, Bahareh; Roshtkhari, Mehrsan Javan; Khorasani, K.

doi:10.1109/tsmc.2017.2705582

Cited by 327 publications

(165 citation statements)

References 32 publications

Supporting

Mentioning

163

Contrasting

Unclassified

Order By: Relevance

“…The gold standard of sleep-disordered diagnosis including conditions such as OSA is polysomnography (PSG). It is used to determine the frequency and severity of normal respiratory disorder events per hour and reports as the Apnea-Hypopnea Index (AHI) which can be used to classify the OSA as normal (AHI<5), mild (AHI is in [5][6][7][8][9][10][11][12][13][14], moderate (AHI is in [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30], and severe (AHI>30), respectively [10]. However, this method is a form of clinical practice which has to be done overnight in a laboratory or hospital [13] using numerous sensors to acquire the necessary data, such as electroencephalogram (EEG), electrooculogram (EOG), chin electromyography (EMG), leg movement, airflow, cannula flow, respiratory effort, oximetry, body position, electrocardiogram (ECG), and so forth [6].…”

Section: Introductionmentioning

confidence: 99%

“…, the training dataset of 1600 samples, 7860 points per sample (15 seconds × 512 Hz), is fed into our model. The model is implemented using Keras with parameter configurations as follows:• A stack of one-dimensional Convolutional Neural Networks (1-D CNNs) with 256, 128 and 64 units, respectively, for automatic feature extraction[20]. • Each CNN layer is followed by batch normalization; the rectified linear unit (ReLU) activation function as well The structure of proposed OSA severity classifier using a Deep Learning approach…”

mentioning

confidence: 99%

See 1 more Smart Citation

Single Channel ECG for Obstructive Sleep Apnea Severity Detection Using a Deep Learning Approach

Banluesombatkul

Rakthanmanon

Wilaiprasitporn

2018

TENCON 2018 - 2018 IEEE Region 10 Conference

View full text Add to dashboard Cite

Obstructive sleep apnea (OSA) is a common sleep disorder caused by abnormal breathing. The severity of OSA can lead to many symptoms such as sudden cardiac death (SCD). Polysomnography (PSG) is a gold standard for OSA diagnosis. It records many signals from the patient's body for at least one whole night and calculates the Apnea-Hypopnea Index (AHI) which is the number of apnea or hypopnea incidences per hour. This value is then used to classify patients into OSA severity levels. However, it has many disadvantages and limitations. Consequently, we proposed a novel methodology of OSA severity classification using a Deep Learning approach. We focused on the classification between normal subjects (AHI < 5) and severe OSA patients (AHI > 30). The 15-second raw ECG records with apnea or hypopnea events were used with a series of one-dimensional Convolutional Neural Networks (1-D CNN) for automatic feature extraction, deep recurrent neural networks with Long Short-Term Memory (LSTM) for temporal information extraction, and fully-connected neural networks (DNN) for feature encoding from a large number of features until it closed to two classes. The main advantages of our proposed method include easier data acquisition, instantaneous OSA severity detection, and effective feature extraction without domain knowledge from expertise. To evaluate our proposed method, 545 subjects of which 364 were normal and 181 were severe OSA patients obtained from the MrOS sleep study (Visit 1) database were used with the kfold cross-validation technique. The accuracy of 79.45% for OSA severity classification with sensitivity, specificity, and F-score was achieved. This is significantly higher than the results from the SVM classifier with RR Intervals and ECG derived respiration (EDR) signal feature extraction. The promising result shows that this proposed method is a good start for the detection of OSA severity from a single channel ECG which can be obtained from wearable devices at home and can also be applied to near realtime alerting systems such as before SCD occurs.

show abstract

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Single Channel ECG for Obstructive Sleep Apnea Severity Detection Using a Deep Learning Approach

Banluesombatkul

Rakthanmanon

Wilaiprasitporn

2018

TENCON 2018 - 2018 IEEE Region 10 Conference

View full text Add to dashboard Cite

show abstract

“…LSTM networks have been successfully applied to PSG processing and were the enabling technology solutions to the top performing systems in the 2018 Computing in Cardiology Physionet competition of identifying non‐apnea arousals from the PSG . They have also been successfully applied to ECG and airflow signals …”

Section: Key Enablers For the Development Of Novel Analysis Methodsmentioning

confidence: 99%

Advanced polysomnographic analysis for OSA: A pathway to personalized management?

2019

View full text Add to dashboard Cite

Obstructive sleep apnea (OSA) is a highly heterogeneous disorder, with diverse pathways to disease, expression of disease, susceptibility to co‐morbidities and response to therapy, and is ideally suited to precision medicine approaches. Clinically, the content of the information‐rich polysomnogram (PSG) is not currently fully utilized in determining patient management. Novel PSG parameters such as hypoxic burden, pulse transit time, cardiopulmonary coupling and the frequency representations of PSG sensor signals could predict a variety of cardiovascular disease, cancer and neurodegeneration co‐morbidities. The PSG can also be used to identify key pathophysiological parameters such as loop gain, arousal threshold and muscle compensation which can enhance understanding of the causes of OSA in an individual, and thereby guide choices on therapy. Machine learning methods performing their own parameter extraction coupled with large PSG data sets offer an exciting opportunity for discovering new links between the PSG variables and disease outcomes. By exploiting existing and emerging analytical methods, the PSG may offer a pathway to personalized management for OSA.

show abstract

“…[307] Artificial intelligence (AI) technologies with the cores of machine learning and big data, have paved a promising way to enable these intelligent applications and services. [305,[308][309][310][311][312][313][314][315][316][317][318][319][320] In machine learning algorithms, computers can automatically learn knowledge from historical experiences data and make prediction on unknown tasks. With the improvement of data acquisition and computing infrastructure, machine learning (especially emerging deep learning) has recently shown or even exceeded the capabilities of human experts in the tasks of image classification, speech recognition, and natural language processing.…”

Section: Machine Learning and Edging Computingmentioning

confidence: 99%

Cyber–Physiochemical Interfaces

et al. 2020

View full text Add to dashboard Cite

Living things rely on various physical, chemical, and biological interfaces, e.g., somatosensation, olfactory/gustatory perception, and nervous system response. They help organisms to perceive the world, adapt to their surroundings, and maintain internal and external balance. Interfacial information exchanges are complicated but efficient, delicate but precise, and multimodal but unisonous, which has driven researchers to study the science of such interfaces and develop techniques with potential applications in health monitoring, smart robotics, future wearable devices, and cyber physical/human systems. To understand better the issues in these interfaces, a cyber–physiochemical interface (CPI) that is capable of extracting biophysical and biochemical signals, and closely relating them to electronic, communication, and computing technology, to provide the core for aforementioned applications, is proposed. The scientific and technical progress in CPI is summarized, and the challenges to and strategies for building stable interfaces, including materials, sensor development, system integration, and data processing techniques are discussed. It is hoped that this will result in an unprecedented multi‐disciplinary network of scientific collaboration in CPI to explore much uncharted territory for progress, providing technical inspiration—to the development of the next‐generation personal healthcare technology, smart sports‐technology, adaptive prosthetics and augmentation of human capability, etc.

show abstract

Deep Convolutional Neural Networks and Learning ECG Features for Screening Paroxysmal Atrial Fibrillation Patients

Cited by 327 publications

References 32 publications

Single Channel ECG for Obstructive Sleep Apnea Severity Detection Using a Deep Learning Approach

Single Channel ECG for Obstructive Sleep Apnea Severity Detection Using a Deep Learning Approach

Advanced polysomnographic analysis for OSA: A pathway to personalized management?

Cyber–Physiochemical Interfaces

Contact Info

Product

Resources

About