Impact of ECG dataset diversity on generalization of CNN model for detecting QRS complex

Detection of QRS complexes in electrocardiogram (ECG) signal is crucial for automated cardiac diagnosis. Automated QRS detection has been a research topic for over three decades and several of the traditional QRS detection methods show acceptable detection accuracy, however, the applicability of these methods beyond their study-specific databases was not explored. The non-stationary nature of ECG and signal variance of intra and inter-patient recordings impose significant challenges on single QRS detectors to achieve reasonable performance. In real life, a promising QRS detector may be expected to achieve acceptable accuracy over diverse ECG recordings and, thus, investigation of the model\u27s generalization capability is crucial. This paper investigates the generalization capability of convolutional neural network (CNN) based-models from intra (subject wise leave-one-out and five-fold cross validation) and inter-database (training with single and multiple databases) points-of-view over three publicly available ECG databases, namely MIT-BIH Arrhythmia, INCART, and QT. Leave-one-out test accuracy reports 99.22%, 97.13%, and 96.25% for these databases accordingly and inter-database tests report more than 90% accuracy with the single exception of INCART. The performance variation reveals the fact that a CNN model\u27s generalization capability does not increase simply by adding more training samples, rather the inclusion of samples from a diverse range of subjects is necessary for reasonable QRS detection accuracy

Precise detection and localization of R-peaks from ECG signals

Heart rate variability (HRV) is derived from the R-R interval, which depends on the precise localization of R-peaks within an electrocardiogram (ECG) signal. However, current algorithm assessment methods prioritize the R-peak detection’s sensitivity rather than the precision of pinpointing the exact R-peak positions. As a result, it is of great value to develop an R-peak detection algorithm with high-precision R-peak localization. This paper introduces a novel R-peak localization algorithm that involves modifications to the well-established Pan-Tompkins (PT) algorithm. The algorithm was implemented as follows. First, the raw ECG signal X(i) was band-pass filtered (5–35 Hz) to obtain a preprocessed signal Y(i). Second, Y(i) was squared to enhance the QRS complex, followed by a 5 Hz low-pass filter to obtain the QRS envelope, which was transformed into a window signal W(i) by dynamic threshold with a minimum width of 200 ms to mark the QRS complex. Third, Y(i) was used to generate QRS template T(n) automatically, and then the R-peak was identified by a template matching process to find the maximum absolute value of all cross-correlation values between T(n) and Y(i). The proposed algorithm achieved a sensitivity (SE) of 99.78%, a positive prediction value (PPV) of 99.78% and data error rate (DER) of 0.44% in R-peak localization for the MIT-BIH Arrhythmia database. The annotated-detected error (ADE), which represents the error between the annotated R-peak location and the detected R-peak location, was 8.35 ms for the MIT-BIH Arrhythmia database. These results outperformed the results obtained using the classical Pan-Tompkins algorithm which yielded an SE of 98.87%, a PPV of 99.14%, a DER of 1.98% and an ADE of 21.65 ms for the MIT-BIH Arrhythmia database. It can be concluded that the algorithm can precisely detect the location of R-peaks and may have the potential to enhance clinical applications of HRV analysis.</p

Precise detection and localization of R-peaks from ECG signals

Heart rate variability (HRV) is derived from the R-R interval, which depends on the precise localization of R-peaks within an electrocardiogram (ECG) signal. However, current algorithm assessment methods prioritize the R-peak detection’s sensitivity rather than the precision of pinpointing the exact R-peak positions. As a result, it is of great value to develop an R-peak detection algorithm with high-precision R-peak localization. This paper introduces a novel R-peak localization algorithm that involves modifications to the well-established Pan-Tompkins (PT) algorithm. The algorithm was implemented as follows. First, the raw ECG signal X(i) was band-pass filtered (5–35 Hz) to obtain a preprocessed signal Y(i). Second, Y(i) was squared to enhance the QRS complex, followed by a 5 Hz low-pass filter to obtain the QRS envelope, which was transformed into a window signal W(i) by dynamic threshold with a minimum width of 200 ms to mark the QRS complex. Third, Y(i) was used to generate QRS template T(n) automatically, and then the R-peak was identified by a template matching process to find the maximum absolute value of all cross-correlation values between T(n) and Y(i). The proposed algorithm achieved a sensitivity (SE) of 99.78%, a positive prediction value (PPV) of 99.78% and data error rate (DER) of 0.44% in R-peak localization for the MIT-BIH Arrhythmia database. The annotated-detected error (ADE), which represents the error between the annotated R-peak location and the detected R-peak location, was 8.35 ms for the MIT-BIH Arrhythmia database. These results outperformed the results obtained using the classical Pan-Tompkins algorithm which yielded an SE of 98.87%, a PPV of 99.14%, a DER of 1.98% and an ADE of 21.65 ms for the MIT-BIH Arrhythmia database. It can be concluded that the algorithm can precisely detect the location of R-peaks and may have the potential to enhance clinical applications of HRV analysis.</p

Detection of multi-class arrhythmia using heuristic and deep neural network on edge device

Heart disease is a heart condition that sometimes causes a person to die suddenly. One indication is a rhythm disorder known as arrhythmia. Multi-class Arrhythmia Detection has followed: QRS complex detection procedure and arrhythmia classification based on the QRS complex morphology. We proposed an edge device that detects QRS complexes based on variance analysis (QVAT) and the arrhythmia classification based on the QRS complex spectrogram. The classifier uses two-dimensional convolutional neural network (2D CNN) deep learning. We use a single board computer and neural network compute stick to implement the edge device. The outcomes are a prototype device cardiologists use as a supporting tool for analysing ECG signals, and patients can also use it for self-tests to figure out their heart health. To evaluate the performance of our edge device, we tested using the MIT-BIH database because other methods also use the data. The QVAT sensitivity and predictive positive are 99.81% and 99.90%, respectively. Our classifier's accuracy, sensitivity, predictive positive, specificity, and F1-score are 99.82%, 99.55%, 99.55%, 99.89%, and 99.55%, respectively. The experiment result of arrhythmia classification shows that our method outperforms the others. Still, for r-peak detection, the QVAT implemented in an edge device is comparable to the other methods. In future work, we can improve the performance of r-peak detection using the double-check algorithm in QVAT and cross-check the QRS complex detection by adding 1 class to the classifier, namely the non-QRS class

Intelligent Methods based on Machine Learning for Classification and Identification of Objects of Interest: A Laboratory Task

Podstatou diplomové práce je návrh exemplárních laboratorních úloh, jejichž cílem je seznámit studující s klasifikací dat za pomocí neuronových sítí. Jednotlivé úlohy se zabývají klasifikací dat. První úkol se věnuje základní metodě klasifikace pomocí perceptronu. Další úkoly se věnují jednak metodě optimalizace neuronové sítě pomocí genetických algoritmů a jednak využití konvolučních neuronových sítí pro klasifikaci jednorozměrných akustických signálů a dvourozměrných obrazů. V jednotlivých úlohách jsou data, pro natrénovaní neuronových sítí, buď dynamicky vytvořena nebo načtena při startu. Následně úlohy demonstrují vytvoření jednotlivých sítí včetně způsobu jejich natrénovaní dle různých vstupních parametrů. V posledních krocích laboratorních úloh se algoritmy validují a výsledky analyzují. Všechny algoritmy dílčích částí byly naprogramovány v prostředí MATLAB s využitím technologie Live Script.The aim of the master thesis is to design exemplary laboratory tasks to introduce students to data classification with neural networks. The individual tasks deal with data classification. The first assignment is about the basic method of classification using perceptron. Other tasks focus on the neural network optimization method using genetic algorithms and the next assignment deals with the use of convolutional neural networks for classification of one-dimensional acoustic signals and two-dimensional images. For each task, data is created or loaded to train the neural networks, then create the network and train it under different settings. In the last step of the lab tasks, the algorithms are validated, and the results are analysed. All the algorithms of the subsections were programmed in MATLAB using Live Script technology.450 - Katedra kybernetiky a biomedicínského inženýrstvívelmi dobř