Spectral analysis of coronary bypass doppler blood flow signals

Dissertação de mest., Engenharia Electrónica e Telecomunicações, Faculdade de Ciências e Tecnologia, Univ. do Algarve, 2011The pulsed Doppler ultrasound (DU) is one of the important tools in the study of vessel diseases and the investigation of flow conditions. Due to its non-invasive nature, it has been increasingly used in medicine in the last few decades. Accurate estimation of DU spectral center frequency and bandwidth parameters are extremely important for blood flow diagnostic purposes. Under real-time data acquisition conditions the DU signal is generally corrupted with different types of noise. In these situations the identification of signal components solely belonging to the blood flow signal is a difficult task. This thesis was aimed to study spectral techniques to enhance spectral parameter estimation, in particular the center frequency. Spectral estimates were obtained using the Short Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT). STFT was applied to short duration data segments, respecting signals’ stationary properties. Two CWT functions have been studied: varying bandwidth filter and fixed bandwidth filter. Since different filter bandwidth values yield different results, bandwidths for fixed bandwidth filter were investigate and the most proper one has been used on the performance comparative studies. To enhance the blood flow signal content of noise-embedded clinical Doppler signals, a STFT-based technique was proposed to reduce the signals’ noise components. Quantitative evaluation of the spectral methods was primarily performed on simulated signals with deterministic center frequency and bandwidth. Different signal to noise ratio signals were simulated. It has been observed that STFT spectral center frequency and bandwidth estimators were less biased than the CWT ones, although the last ones were less sensitive to the center frequency variations. Applying the proposed noise cancellation technique to simulated signals reduces the spectral estimators’ errors. As an example, a typical noisy signal with 10dbSNR, a reduction of 88% and 97% was obtained on the RMS bias of the estimation of the center frequency and bandwidth estimators respectively

Characterization, Classification, and Genesis of Seismocardiographic Signals

Seismocardiographic (SCG) signals are the acoustic and vibration induced by cardiac activity measured non-invasively at the chest surface. These signals may offer a method for diagnosing and monitoring heart function. Successful classification of SCG signals in health and disease depends on accurate signal characterization and feature extraction. In this study, SCG signal features were extracted in the time, frequency, and time-frequency domains. Different methods for estimating time-frequency features of SCG were investigated. Results suggested that the polynomial chirplet transform outperformed wavelet and short time Fourier transforms. Many factors may contribute to increasing intrasubject SCG variability including subject posture and respiratory phase. In this study, the effect of respiration on SCG signal variability was investigated. Results suggested that SCG waveforms can vary with lung volume, respiratory flow direction, or a combination of these criteria. SCG events were classified into groups belonging to these different respiration phases using classifiers, including artificial neural networks, support vector machines, and random forest. Categorizing SCG events into different groups containing similar events allows more accurate estimation of SCG features. SCG feature points were also identified from simultaneous measurements of SCG and other well-known physiologic signals including electrocardiography, phonocardiography, and echocardiography. Future work may use this information to get more insights into the genesis of SCG

Classification of the mechanomyogram signal using a wavelet packet transform and singular value decomposition

Title on author’s file: Classification of mechanomyogram signal using wavelet packet transform and singular value decomposition for multifunction prosthesis control2008-2009 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

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

Design, Evaluation, and Application of Heart Rate Variability Analysis Software (HRVAS)

The analysis of heart rate variability (HRV) has become an increasingly popular and important tool for studying many disease pathologies in the past twenty years. HRV analyses are methods used to non-invasively quantify variability within heart rate. Purposes of this study were to design, evaluate, and apply an easy to use and open-source HRV analysis software package (HRVAS). HRVAS implements four major categories of HRV techniques: statistical and time-domain analysis, frequency-domain analysis, nonlinear analysis, and time-frequency analysis. Software evaluations were accomplished by performing HRV analysis on simulated and public congestive heart failure (CHF) data. Application of HRVAS included studying the effects of hyperaldosteronism on HRV in rats. Simulation and CHF results demonstrated that HRVAS was a dependable HRV analysis tool. Results from the rat hyperaldosteronism model showed that 5 of 26 HRV measures were statistically significant (p\u3c0.05). HRVAS provides a useful tool for HRV analysis to researchers

Investigations of the neural mechanisms of cardiac stability

Electrical instability of the heart is known to precede the onset of lethal arrhythmias and the autonomic nervous system (ANS) is a primary factor in this process. However, the exact mechanisms of failure remain poorly understood. This work aims to better understand how ANS activity affects the electrical properties of the heart by investigating the effect of autonomic rhythms on the ventricular action potential duration (APD) recorded at tissue level using unipolar electrograms (UEGs). Studying dynamic behaviour of APD was associated with large data-sets of UEGs. Methods were developed to improve accuracy of automatic detection of APD, like narrow search windows and correlation filters to detect ambiguous activity. A simulation study was conducted to generate realistic UEG recordings to examine the effect of signal quality and filtering on tracking of APD dynamics. New insights were provided in how signal quality and filtering affect the accuracy of APD tracking. The proposed improvements were found to reduce the detection error substantially. The effect of autonomic rhythms on ventricular APD was explored using existing clinical data. By employing techniques to determine causality and time-frequency coherence, evidence was found that the ANS modulates ventricular electrophysiology: (1) with respiratory behaviour via a direct causal pathway, and (2) at a lower frequency and related to signs of enhanced sympathetic activity in blood pressure observed during mental stress. Further investigations were undertaken by designing and conducting a clinical experiment to study the effect of baroreceptor control on APD. Novel methodologies to determine the statistical significance of response curves were used to demonstrate for the first time that ventricular APD can be influenced by baroreceptor stimulation independent of heart rate. Identification of the neural mechanisms controlling cardiac stability may ultimately contribute to the development of new diagnostic tools and treatments to prevent thousands of deaths each year

Biomedical Signal and Image Processing

Written for senior-level and first year graduate students in biomedical signal and image processing, this book describes fundamental signal and image processing techniques that are used to process biomedical information. The book also discusses application of these techniques in the processing of some of the main biomedical signals and images, such as EEG, ECG, MRI, and CT. New features of this edition include the technical updating of each chapter along with the addition of many more examples, the majority of which are MATLAB based

Heart Rate Variability analysis in patients undergoing local anesthesia

The analysis of Heart Rate Variability (HRV), the beat to beat fluctuation in the heart rate, is a non-invasive technique with a main aim in gaining information about the autonomic neural regulation of the heart. Assessment of HRV has been shown to aid clinical diagnosis and intervention strategies. However, there are quite a few conflicting reports on HRV that perhaps impede its use as a reliable clinical tool. The complex nature of different mechanisms that affect the HRV and the large number of signal processing techniques that have been used for HRV analysis are the contributing factors of these conflicting results. The aim of this study was to investigate for the first time the effect of HRV during Brachial plexus block (local anaesthesia), applied using the axillary approach. The hypothesis was that, such investigation will enable the detection of possible changes in the dynamics of the cardiovascular system due to the intravenous introduction of anaesthetic drugs during local anaesthesia. For this purpose advanced HRV signals processing techniques were developed and evaluated on data collected before and after the application of the Brachial plexus block from fourteen patients undergoing local anaesthesia. Signal processing techniques for R-wave detection, signal representation, ectopic beat detection and detrending were first developed and validated with the help of simulated signals and physiological signals from Physionet data base. After the validation stage these methods were then used to analyse the data from the locally anaesthetised patients. The ECG R-wave peak detection was carried out using the wavelet transform with first derivative of Gaussian smoothing function as the mother wavelet. The algorithm achieved accuracy and sensitivity of over 90%. The heart timing signal was used for the HRV signal representation and also for the correction of missing and/or ectopic beats. The results obtained from the ectopic beat correction algorithm showed that the algorithm managed to significantly reduce the error caused by missing and/or ectopic beats. Detrending of the HRV signal was carried out using the wavelet packet analysis algorithm which was specifically developed for this study. The respiration signal was also estimaited from the ECG signal using the ECG Derived Respiration (EDR) technique. In order to take better account of slow respiration rates and/or irregular respiratory patterns in the HRV analysis, a new method for the estimation of the variable boundaries associated with the LF and the HF band of the HRV signal was implemented. This method relies on the frequency contents of both the HRV signal and the respiration signal and uses the cross-spectrum between these two signals to obtain the boundaries related to the HF band of the signal. The boundaries related to the LF band were defined using the HRV signal spectrum alone. The boundary estimation technique was applicable in all the spectral analysis methods that were used in this study. After the pre-processing steps the clinical data was analysed using frequency and timefrequency analysis methods to obtain the parameters related to the HRV signals. Initially spectral analysis was carried out using the traditional non-parametric (Welch’s periodogram) and parametric (Autoregressive modelling) methods. Statistical analysis of the parameters obtained from both the non-parametric and the parametric methods showed significant decrease in the LF/HF ratio values within an hour of application of the block in nine out of fourteen patients. In order to overcome the inability of these methods to deal with non-stationary, time-frequency analysis techniques were used to further analyse the HRV signals. The three time-frequency analysis methods used were the ContinuousWavelet Transform (CWT), theWigner-Ville Distribution (SPWVD) and the Empirical Mode Decomposition (EMD). The analysis of the parameters estimated from these three techniques on the clinical data showed that the CWT and the EMD techniques have performed equivalently, meaning that both these methods have detected significant decrease in thirteen out of fourteen patients for the ratio values after the application of the,anaesthetic block. The presence of interference terms has caused the degradation in the performance of the SPWVD method and due to this reason it was only able to detect significant changes in the LF/HF ratio values in ten of the fourteen patients. The results suggest that due to anxiety and/or adrenaline present in the local anaesthetic mixture, the LF/HF ratio values showed a transient increase shortly after the application of the block. After this transient increase the ratio values decreased considerably and remained low as compared to the values before the application of the block. This decrease could represent the shift of the sympathovagal balance towards parasympathetic predominance and/or inhabitation of sympathetic activity due to local anaesthesia. The use of timefrequency analysis such as EMD and CWT could provide useful information about the changes caused in the dynamics of the cardiovascular system when a local anaesthetic drug is administered in a patient