Key result
A machine learning approach using a k-nearest neighbours classifier achieved 98.2% accuracy in automatically detecting and classifying bundle branch blocks from ECG signals.
Why the study?
Bundle branch block beats can indicate serious heart disease, and precise, quick automated detection from ECG signals can save lives and reduce diagnostic costs.
Does a machine learning technique using statistical and morphological ECG features accurately detect and classify bundle branch blocks?
Does a machine learning technique using statistical and morphological ECG features accurately detect and classify bundle branch blocks?
A machine learning approach using k-nearest neighbours on statistical and morphological ECG features can accurately detect and classify bundle branch blocks with 98.2% accuracy.
The most effective method used for the diagnosis of heart diseases is the Electrocardiogram (ECG). The shape of the ECG signal and the time interval between its various components gives useful details about any underlying heart disease. Any dysfunction of the heart is called as cardiac arrhythmia. The electrical impulses of the heart are blocked due to the cardiac arrhythmia called Bundle Branch Block (BBB) which can be observed as an irregular ECG wave. The BBB beats can indicate serious heart disease. The precise and quick detection of cardiac arrhythmias from the ECG signal can save lives and can also reduce the diagnostics cost. This study presents a machine learning technique for the automatic detection of BBB. In this method both morphological and statistical features were calculated from the ECG signals available in the standard MIT BIH database to classify them as normal, Left Bundle Branch Block (LBBB) and Right Bundle Branch Block (RBBB). ECG records in the MIT- BIH arrhythmia database containing Normal sinus rhythm, RBBB, and LBBB were used in the study. The suitability of the features extracted was evaluated using three classifiers, support vector machine, k-nearest neighbours and linear discriminant analysis. The accuracy of the technique is highly promising for all the three classifiers with k-nearest neighbours giving the highest accuracy of 98.2%. Since the ECG waveforms of patients with the same cardiac disorder is similar in shape, the proposed method is subject independent. The proposed technique is thus a reliable and simple method involving less computational complexity for the automatic detection of bundle branch block. This system can reduce the effort of cardiologists thereby enabling them to concentrate more on treatment of the patients.
No takes yet. Share an insight, caveat, or question.
Kammath et al. (2022) studied Bundle Branch Block (n=47). k-Nearest Neighbours (kNN) classifier vs. Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) was evaluated on Classification accuracy. A machine learning approach using a k-nearest neighbours classifier achieved 98.2% accuracy in automatically detecting and classifying bundle branch blocks from ECG signals.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: