Tensor-Based ECG Anomaly Detection toward Cardiac Monitoring in the Internet of Health Things

Abstract

Advanced heart monitors, especially those enabled by the Internet of Health Things (IoHT), provide a great opportunity for continuous collection of the electrocardiogram (ECG), which contains rich information about underlying cardiac conditions. Realizing the full potential of IoHT-enabled cardiac monitoring hinges, to a great extent, on the detection of disease-induced anomalies from collected ECGs. However, challenges exist in the current literature for IoHT-based cardiac monitoring: (1) Most existing methods are based on supervised learning, which requires both normal and abnormal samples for training. This is impractical as it is generally unknown when and what kind of anomalies will occur during cardiac monitoring. (2) Furthermore, it is difficult to leverage advanced machine learning approaches for information processing of 1D ECG signals, as most of them are designed for 2D images and higher-dimensional data. To address these challenges, a new sensor-based unsupervised framework is developed for IoHT-based cardiac monitoring. First, a high-dimensional tensor is generated from the multi-channel ECG signals through the Gramian Angular Difference Field (GADF). Then, multi-linear principal component analysis (MPCA) is employed to unfold the ECG tensor and delineate the disease-altered patterns. Obtained principal components are used as features for anomaly detection using machine learning models (e.g., deep support vector data description (deep SVDD)) as well as statistical control charts (e.g., Hotelling T2 chart). The developed framework is evaluated and validated using real-world ECG datasets. Comparing to the state-of-the-art approaches, the developed framework with deep SVDD achieves superior performances in detecting abnormal ECG patterns induced by various types of cardiac disease, e.g., an F-score of 0.9771 is achieved for detecting atrial fibrillation, 0.9986 for detecting right bundle branch block, and 0.9550 for detecting ST-depression. Additionally, the developed framework with the T2 control chart facilitates personalized cycle-to-cycle monitoring with timely detected abnormal ECG patterns. The developed framework has a great potential to be implemented in IoHT-enabled cardiac monitoring and smart management of cardiac health.

Possibilities of Using Artificial Intelligence to Perform Ecg Analysis During Population Screening

“…Автори [22] застосували класифікатор Support Vector Machine після вейвлетвилучення особливостей варіативності серцевого ритму до класифікації далекого ритму. Багатолінійний аналіз головних компонентів використовується для обробки ЕКГ для виділення змінених хворобою патернів з подальшим виявленням аномалій з використанням глибокого опорного векторного опису даних у [23]. Запропонована система забезпечує чудову продуктивність у виявленні патологічних синдромів ЕКГ, таких як фібриляція передсердь, блокада правої ніжки пучка Гіса та депресія ST. Останнім часом мережеве застосування глибокого навчання привертає більше уваги для виявлення аномалій ЕКГ.…”

ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration

“…Some of these studies [ [45] , [46] , [47] ] employed 1-D ECG signals to classify the anomalies. Other research articles [ [48] , [49] , [50] , [51] , [52] ] transformed ECG signals to 2-D images using numerous methods such as wavelet transform and short-term frequency transform to detect abnormalities. Although the previous methods achieved significant success using public ECG signals-based datasets, it would be difficult to apply them in the real-world medical environment.…”

Detection of Abnormal Cardiac Response Patterns in Cardiac Tissue Using Deep Learning

“…Regarding deep learning algorithms, the most frequently used for the detection of ECG signal anomalies is long short-term memory (LSTM) [25]. Convolutional neural networks (CNNs) [25][26][27][28][29] can also be used after the conversion of the signal into a spectrogram image using wavelet transform or short-time Fourier transform [30]. The good results obtained in two-dimensional convolutional neural networks (2D CNNs) in computer vision applications, such as AlexNet, VGG16 or ResNet networks, together with the emergence of the large datasets CIFAR100 and ImageNet have prompted the signal processing community to have a renewed interest in 1D CNNs [31].…”