Key result
A k-nearest neighbors machine-learning model using ECG features classified stroke patients with 96.6% accuracy, 94.3% precision, and 99.1% recall.
Why the study?
ECG changes and autonomic dysfunction occur in stroke patients, prompting analysis of ECG features to develop machine-learning classification models for diagnosing stroke.
Can ECG features combined with machine learning models accurately classify post-stroke survivors compared to healthy controls?
Case-Control (n=132)
Can ECG features combined with machine learning models accurately classify post-stroke survivors compared to healthy controls?
Machine learning models, particularly KNN, using ECG features can accurately classify post-stroke survivors, highlighting the potential of ECG as a diagnostic tool for stroke-related autonomic dysfunction.
ECG-ML classification shows promise in case-control settings; leaves open prospective validation before clinical use.
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.
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Rathakrishnan et al. (2020) conducted a case-control in Stroke (n=132). ECG feature analysis and machine-learning classification vs. Control subjects was evaluated on Stroke classification accuracy using the k-nearest neighbors (KNN) model. A k-nearest neighbors machine-learning model using ECG features classified stroke patients with 96.6% accuracy, 94.3% precision, and 99.1% recall.
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