Evaluation of ECG Features for the Classification of Post-Stroke Survivors with a Diagnostic Approach

Abstract

Stroke is considered as a major cause of death and neurological disorders commonly associated with elderly people. Electrocardiogram (ECG) signals are used as a powerful tool in diagnosing stroke, and the analysis of ECG signals has become the focus of stroke research. ECG changes and autonomic dysfunction are reportedly seen in patients with stroke. This study aimed to analyze the ECG features and develop a classification model with highly ranked ECG features as input variables based on machine-learning techniques for diagnosing stroke disease. The study included 52 stroke patients (mean age 72.7 years, 63% male) and 80 control subjects (mean age 75.5 years, 39% male) for a total of 132 elderly subjects. Resting ECG signals in the lying down position are measured using the BIOPAC MP150 system. The ECG signals are denoised using the discrete wavelet transform (DWT) method, and the features such as heart rate variability (HRV), indices of time and spectral domains and statistical and impulsive metrics, in addition to fiducial features, are extracted and analyzed. Our results showed that the values of the HRV variables were lower in the stroke group, revealing autonomic dysfunction in stroke patients. A statistically significant difference was observed in low-frequency (LF)/high-frequency (HF), time interval measured after the S wave to the beginning of the T wave (ST) and time interval measured from the beginning of the Q wave to the end of the T wave (QT) (p < 0.05) between the groups. Our study also highlighted some of the risk factors of stroke, such as age, male sex and dyslipidemia (p < 0.05), that are statistically significant. The k-nearest neighbors (KNN) model showed the highest classification results (accuracy 96.6%, precision 94.3%, recall 99.1% and F1-score 96.6%) than the random forest, support vector machine (SVM), Naïve Bayes and logistic regression models. Thus, our study reported some of the notable ECG changes in the study participants and also indicated that ECG could aid in diagnosing stroke disease.

Explainable machine learning methods and respiratory oscillometry for the diagnosis of respiratory abnormalities in sarcoidosis

“…Previous research has established that diagnostic easiness is a fundamental attribute for occupied non-specialist clinicians [ 53 ]. Studies in radiology [ 54 ], ophthalmology [ 55 ], and cardiology [ 56 ] have shown that ML methods may contribute to improving the medical service by AI-assisted workflow. The present study confirms and extends these findings to respiratory physiology showing that machine learning algorithms help diagnose respiratory abnormalities in sarcoidosis.…”

Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review

“…Indeed, wearable sensors allow patients to execute their exercises at home relieving them of the drain of transportation. Subsequently, several types of sensing devices are used in applications extending from monitoring subjects' physiologic responses like Electromyography (EMG) [30], Electrocardiogram (ECG) [31], or glucose level in the blood [32] to evaluating kinematics of the individuals: gait, ROM, balance using Inertial Measurement Units (IMU) [33]. These sensors are employed in conjunction with clinical tests and outcome measures, such as sit-to-stand [34], Timed Up and Go (TUG) [35] to give an objective assessment and monitoring of the patient condition [36].…”