2 research outputs found
Automatic analysis and classification of cardiac acoustic signals for long term monitoring
Objective: Cardiovascular diseases are the leading cause of death worldwide resulting in over 17.9 million deaths each year. Most of these diseases are preventable and treatable, but their progression and outcomes are significantly more positive with early-stage diagnosis and proper disease management. Among the approaches available to assist with the task of early-stage diagnosis and management of cardiac conditions, automatic analysis of auscultatory recordings is one of the most promising ones, since it could be particularly suitable for ambulatory/wearable monitoring. Thus, proper investigation of abnormalities present in cardiac acoustic signals can provide vital clinical information to assist long term monitoring. Cardiac acoustic signals, however, are very susceptible to noise and artifacts, and their characteristics vary largely with the recording conditions which makes the analysis challenging. Additionally, there are challenges in the steps used for automatic analysis and classification of cardiac acoustic signals. Broadly, these steps are the segmentation, feature extraction and subsequent classification of recorded signals using selected features. This thesis presents approaches using novel features with the aim to assist the automatic early-stage detection of cardiovascular diseases with improved performance, using cardiac acoustic signals collected in real-world conditions.
Methods: Cardiac auscultatory recordings were studied to identify potential features to help in the classification of recordings from subjects with and without cardiac diseases. The diseases considered in this study for the identification of the symptoms and characteristics are the valvular heart diseases due to stenosis and regurgitation, atrial fibrillation, and splitting of fundamental heart sounds leading to additional lub/dub sounds in the systole or diastole interval of a cardiac cycle. The localisation of cardiac sounds of interest was performed using an adaptive wavelet-based filtering in combination with the Shannon energy envelope and prior information of fundamental heart sounds. This is a prerequisite step for the feature extraction and subsequent classification of recordings, leading to a more precise diagnosis. Localised segments of S1 and S2 sounds, and artifacts, were used to extract a set of perceptual and statistical features using wavelet transform, homomorphic filtering, Hilbert transform and mel-scale filtering, which were then fed to train an ensemble classifier to interpret S1 and S2 sounds. Once sound peaks of interest were identified, features extracted from these peaks, together with the features used for the identification of S1 and S2 sounds, were used to develop an algorithm to classify recorded signals. Overall, 99 features were extracted and statistically analysed using neighborhood component analysis (NCA) to identify the features which showed the greatest ability in classifying recordings. Selected features were then fed to train an ensemble classifier to classify abnormal recordings, and hyperparameters were optimized to evaluate the performance of the trained classifier. Thus, a machine learning-based approach for the automatic identification and classification of S1 and S2, and normal and abnormal recordings, in real-world noisy recordings using a novel feature set is presented. The validity of the proposed algorithm was tested using acoustic signals recorded in real-world, non-controlled environments at four auscultation sites (aortic valve, tricuspid valve, mitral valve, and pulmonary valve), from the subjects with and without cardiac diseases; together with recordings from the three large public databases. The performance metrics of the methodology in relation to classification accuracy (CA), sensitivity (SE), precision (P+), and F1 score, were evaluated.
Results: This thesis proposes four different algorithms to automatically classify fundamental heart sounds – S1 and S2; normal fundamental sounds and abnormal additional lub/dub sounds recordings; normal and abnormal recordings; and recordings with heart valve disorders, namely the mitral stenosis (MS), mitral regurgitation (MR), mitral valve prolapse (MVP), aortic stenosis (AS) and murmurs, using cardiac acoustic signals. The results obtained from these algorithms were as follows:
• The algorithm to classify S1 and S2 sounds achieved an average SE of 91.59% and 89.78%, and F1 score of 90.65% and 89.42%, in classifying S1 and S2, respectively. 87 features were extracted and statistically studied to identify the top 14 features which showed the best capabilities in classifying S1 and S2, and artifacts. The analysis showed that the most relevant features were those extracted using Maximum Overlap Discrete Wavelet Transform (MODWT) and Hilbert transform.
• The algorithm to classify normal fundamental heart sounds and abnormal additional lub/dub sounds in the systole or diastole intervals of a cardiac cycle, achieved an average SE of 89.15%, P+ of 89.71%, F1 of 89.41%, and CA of 95.11% using the test dataset from the PASCAL database. The top 10 features that achieved the highest weights in classifying these recordings were also identified.
• Normal and abnormal classification of recordings using the proposed algorithm achieved a mean CA of 94.172%, and SE of 92.38%, in classifying recordings from the different databases. Among the top 10 acoustic features identified, the deterministic energy of the sound peaks of interest and the instantaneous frequency extracted using the Hilbert Huang-transform, achieved the highest weights.
• The machine learning-based approach proposed to classify recordings of heart valve disorders (AS, MS, MR, and MVP) achieved an average CA of 98.26% and SE of 95.83%. 99 acoustic features were extracted and their abilities to differentiate these abnormalities were examined using weights obtained from the neighborhood component analysis (NCA). The top 10 features which showed the greatest abilities in classifying these abnormalities using recordings from the different databases were also identified.
The achieved results demonstrate the ability of the algorithms to automatically identify and classify cardiac sounds. This work provides the basis for measurements of many useful clinical attributes of cardiac acoustic signals and can potentially help in monitoring the overall cardiac health for longer duration. The work presented in this thesis is the first-of-its-kind to validate the results using both, normal and pathological cardiac acoustic signals, recorded for a long continuous duration of 5 minutes at four different auscultation sites in non-controlled real-world conditions.Open Acces
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ECG analysis and classification using CSVM, MSVM and SIMCA classifiers
Reliable ECG classification can potentially lead to better detection methods and increase
accurate diagnosis of arrhythmia, thus improving quality of care. This thesis investigated the
use of two novel classification algorithms: CSVM and SIMCA, and assessed their
performance in classifying ECG beats. The project aimed to introduce a new way to
interactively support patient care in and out of the hospital and develop new classification
algorithms for arrhythmia detection and diagnosis. Wave (P-QRS-T) detection was performed
using the WFDB Software Package and multiresolution wavelets. Fourier and PCs were
selected as time-frequency features in the ECG signal; these provided the input to the
classifiers in the form of DFT and PCA coefficients. ECG beat classification was performed
using binary SVM. MSVM, CSVM, and SIMCA; these were subsequently used for
simultaneously classifying either four or six types of cardiac conditions. Binary SVM
classification with 100% accuracy was achieved when applied on feature-reduced ECG
signals from well-established databases using PCA. The CSVM algorithm and MSVM were
used to classify four ECG beat types: NORMAL, PVC, APC, and FUSION or PFUS; these
were from the MIT-BIH arrhythmia database (precordial lead group and limb lead II).
Different numbers of Fourier coefficients were considered in order to identify the optimal
number of features to be presented to the classifier. SMO was used to compute hyper-plane
parameters and threshold values for both MSVM and CSVM during the classifier training
phase. The best classification accuracy was achieved using fifty Fourier coefficients. With the
new CSVM classifier framework, accuracies of 99%, 100%, 98%, and 99% were obtained
using datasets from one, two, three, and four precordial leads, respectively. In addition, using
CSVM it was possible to successfully classify four types of ECG beat signals extracted from
limb lead simultaneously with 97% accuracy, a significant improvement on the 83% accuracy
achieved using the MSVM classification model. In addition, further analysis of the following
four beat types was made: NORMAL, PVC, SVPB, and FUSION. These signals were
obtained from the European ST-T Database. Accuracies between 86% and 94% were obtained
for MSVM and CSVM classification, respectively, using 100 Fourier coefficients for
reconstructing individual ECG beats. Further analysis presented an effective ECG arrhythmia
classification scheme consisting of PCA as a feature reduction method and a SIMCA
classifier to differentiate between either four or six different types of arrhythmia. In separate
studies, six and four types of beats (including NORMAL, PVC, APC, RBBB, LBBB, and
FUSION beats) with time domain features were extracted from the MIT-BIH arrhythmia
database and the St Petersburg INCART 12-lead Arrhythmia Database (incartdb) respectively.
Between 10 and 30 PCs, coefficients were selected for reconstructing individual ECG beats in
the feature selection phase. The average classification accuracy of the proposed scheme was
98.61% and 97.78 % using the limb lead and precordial lead datasets, respectively. In addition,
using MSVM and SIMCA classifiers with four ECG beat types achieved an average
classification accuracy of 76.83% and 98.33% respectively. The effectiveness of the proposed
algorithms was finally confirmed by successfully classifying both the six beat and four beat
types of signal respectively with a high accuracy ratio