Signal processing methodologies for an acoustic fetal heart rate monitor

Research and development is presented of real time signal processing methodologies for the detection of fetal heart tones within a noise-contaminated signal from a passive acoustic sensor. A linear predictor algorithm is utilized for detection of the heart tone event and additional processing derives heart rate. The linear predictor is adaptively 'trained' in a least mean square error sense on generic fetal heart tones recorded from patients. A real time monitor system is described which outputs to a strip chart recorder for plotting the time history of the fetal heart rate. The system is validated in the context of the fetal nonstress test. Comparisons are made with ultrasonic nonstress tests on a series of patients. Comparative data provides favorable indications of the feasibility of the acoustic monitor for clinical use

Heart sound monitoring sys

Cardiovascular disease (CVD) is among the leading life threatening ailments [1] [2].Under normal circumstances, a cardiac examination utilizing electrocardiogram appliances or tools is proposed for a person stricken with a heart disorder. The logging of irregular heart behaviour and morphology is frequently achieved through an electrocardiogram (ECG) produced by an electrocardiographic appliance for tracing cardiac activity. For the most part, gauging of this activity is achieved through a non-invasive procedure i.e. through skin electrodes. Taking into consideration the ECG and heart sound together with clinical indications, the cardiologist arrives at a diagnosis on the condition of the patient's heart. This paper focuses on the concerns stated above and utilizes the signal processing theory to pave the way for better heart auscultation performance by GPs. The objective is to take note of heart sounds in correspondence to the valves as these sounds are a source of critical information. Comparative investigations regarding MFCC features with varying numbers of HMM states and varying numbers of Gaussian mixtures were carried out for the purpose of determining the impact of these features on the classification implementation at the sites of heart sound auscultation. We employ new strategy to evaluate and denoise the heart and ecg signal with a specific end goal to address specific issues

Analysis of Respiratory Sounds: State of the Art

Objective This paper describes state of the art, scientific publications and ongoing research related to the methods of analysis of respiratory sounds. Methods and material Review of the current medical and technological literature using Pubmed and personal experience. Results The study includes a description of the various techniques that are being used to collect auscultation sounds, a physical description of known pathologic sounds for which automatic detection tools were developed. Modern tools are based on artificial intelligence and on technics such as artificial neural networks, fuzzy systems, and genetic algorithms… Conclusion The next step will consist in finding new markers so as to increase the efficiency of decision aid algorithms and tools

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

Machine Learning-Based Classification of Pulmonary Diseases through Real-Time Lung Sounds

    The study presents a computer-based automated system that employs machine learning to classify pulmonary diseases using lung sound data collected from hospitals. Denoising techniques, such as discrete wavelet transform and variational mode decomposition, are applied to enhance classifier performance. The system combines cepstral features, such as Mel-frequency cepstrum coefficients and gammatone frequency cepstral coefficients, for classification. Four machine learning classifiers, namely the decision tree, k-nearest neighbor, linear discriminant analysis, and random forest, are compared. Evaluation metrics such as accuracy, recall, specificity, and f1 score are employed. This study includes patients affected by chronic obstructive pulmonary disease, asthma, bronchiectasis, and healthy individuals. The results demonstrate that the random forest classifier outperforms the others, achieving an accuracy of 99.72% along with 100% recall, specificity, and f1 scores. The study suggests that the computer-based system serves as a decision-making tool for classifying pulmonary diseases, especially in resource-limited settings

The Heart Auscultation. From Sound to Graphical

Heart sounds and murmurs have very small amplitude and frequency signals thus make it so difficult to hear without the correct tools. In clinical practice currently, physicians listen to the patient heart sound and murmurs by using the traditional technique as an example mechanical stethoscope which having low accuracy and could lead to the false diagnosis. Moreover, conventional method has no ability to record the sound measured. Worst still it is totally depending on the physician’s skills and experienced which this ability is decreased over time. This issue is highly important in early detection of heart sound abnormal. The stereo heart auscultation purposed in this research is to provide solutions rise from conventional technique. Furthermore, the sound signals produced from heart will be converted to the real-time graphically presented with time-frequency analysis, which provides more information about the heart conditions by sound produced. The system compromise hardware such as electrical transducer, electronic circuit, data-acquisition device, computer and also software for signal visualization or imaging. Database of heart sound and murmurs use to validate the developmental system replacing true patients. It has been demonstrated, in preliminary result, that heart sound classification according to on types of a valve problem such as aortic regurgitation, mitral regurgitation, tricuspid regurgitation, aortic stenosis and pulmonic stenosis could be differentiated using the development measurement system

Assessment of Dual-Tree Complex Wavelet Transform to improve SNR in collaboration with Neuro-Fuzzy System for Heart Sound Identification

none6siThe research paper proposes a novel denoising method to improve the outcome of heartsound (HS)-based heart-condition identification by applying the dual-tree complex wavelet transform (DTCWT) together with the adaptive neuro-fuzzy inference System (ANFIS) classifier. The method consists of three steps: first, preprocessing to eliminate 50 Hz noise; second, applying four successive levels of DTCWT to denoise and reconstruct the time-domain HS signal; third, to evaluate ANFIS on a total of 2735 HS recordings from an international dataset (PhysioNet Challenge 2016). The results show that the signal-to-noise ratio (SNR) with DTCWT was significantly improved (p < 0.001) as compared to original HS recordings. Quantitatively, there was an 11% to many decibel (dB)-fold increase in SNR after DTCWT, representing a significant improvement in denoising HS. In addition, the ANFIS, using six time-domain features, resulted in 55–86% precision, 51–98% recall, 53–86% f-score, and 54–86% MAcc compared to other attempts on the same dataset. Therefore, DTCWT is a successful technique in removing noise from biosignals such as HS recordings. The adaptive property of ANFIS exhibited capability in classifying HS recordings.Special Issue “Biomedical Signal Processing”, Section BioelectronicsopenBassam Al-Naami, Hossam Fraihat, Jamal Al-Nabulsi, Nasr Y. Gharaibeh, Paolo Visconti, Abdel-Razzak Al-HinnawiAl-Naami, Bassam; Fraihat, Hossam; Al-Nabulsi, Jamal; Gharaibeh, Nasr Y.; Visconti, Paolo; Al-Hinnawi, Abdel-Razza