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A topological approach to delineation and arrhythmic beats detection in unprocessed long-term ECG signals
Jana Faganeli Pucer1, Matjaž Kukar1
1Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, Ljubljana 1000, Slovenia.
Insights
This study introduces a novel Morse theory-based algorithm for detecting arrhythmias in ambulatory electrocardiogram (AECG) recordings. The method achieves high accuracy in classifying normal and abnormal heartbeats from raw ECG signals in near real-time.
Area of Science:
- Cardiology and Biomedical Signal Processing
- Application of Topological Principles in Medical Diagnostics
Background:
- Arrhythmias are common in cardiac failure and typically diagnosed via electrocardiogram (ECG) recordings.
- Manual interpretation of long ambulatory ECG (AECG) recordings is time-consuming due to their duration and scarcity of arrhythmia events.
- Automated systems are crucial for efficient AECG anomaly detection.
Purpose of the Study:
- To present a novel procedure for detecting arrhythmic beats in AECG using topological principles (Morse theory).
- To develop an automated system capable of processing raw ECG signals in nearly real-time.
- To reduce the preprocessing burden associated with traditional ECG analysis methods.
Main Methods:
- Utilized a subject-specific adaptation of one-dimensional discrete Morse theory (ADMT) to represent ECG signals via extrema.
- Applied ADMT for noise removal and characteristic ECG beat wave detection and annotation.
- Employed a decision tree classifier using beat similarity measures (distance and shape difference) for normal/abnormal classification.
Main Results:
- Achieved a classification accuracy of 92.73% on the MIT-BIH database.
- Demonstrated high performance metrics: 73.35% sensitivity, 96.70% specificity, 88.01% positive predictive value, and 95.73% negative predictive value.
- The algorithm operates with a 14-second delay, enabling near real-time analysis.
Conclusions:
- The novel Morse theory-based algorithm effectively detects and classifies arrhythmic beats in AECG.
- Requires less preprocessing compared to existing methods while maintaining near real-time performance.
- Offers accuracy comparable to state-of-the-art automated arrhythmia detection systems.
Background And Objective:
Arrhythmias are one of the most common symptoms of cardiac failure. They are usually diagnosed using ECG recordings, particularly long ambulatory recordings (AECG). These recordings are tedious to interpret by humans due to their extent (up to 48 h) and the relative scarcity of arrhythmia events. This makes automated systems for detecting various AECG anomalies indispensable. In this work we present a novel procedure based on topological principles (Morse theory) for detecting arrhythmic beats in AECG. It works in nearly real-time (delayed by a 14 s window), and can be applied to raw (unprocessed) ECG signals.
Methods:
The procedure is based on a subject-specific adaptation of the one-dimensional discrete Morse theory (ADMT), which represents the signal as a sequence of its most important extrema. The ADMT algorithm is applied twice; for low-amplitude, high-frequency noise removal, and for detection of the characteristic waves of individual ECG beats. The waves are annotated using the ADMT algorithm and template matching. The annotated beats are then compared to the adjacent beats with two measures of similarity: the distance between two beats, and the difference in shape between them. The two measures of similarity are used as inputs to a decision tree algorithm that classifies the beats as normal or abnormal. The classification performance is evaluated with the leave-one-record-out cross-validation method.
Results:
Our approach was tested on the MIT-BIH database, where it exhibited a classification accuracy of 92.73%, a sensitivity of 73.35%, a specificity of 96.70%, a positive predictive value of 88.01%, and a negative predictive value of 95.73%.
Conclusions:
Compared to related studies, our algorithm requires less preprocessing while retaining the capability to detect and classify beats in almost real-time. The algorithm exhibits a high degree of accuracy in beats detection and classification that are at least comparable to state-of-the-art methods.
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