2. A modified model of heart-arterial system was proposed for describing the timing of the second heart sound as a result of the heart-arterial interaction. Simulation results suggest that RS2 bears a significant negative correlation with both SBP and DBP as heart rate, cardiac contractility and peripheral resistance varies. The hypothesis was supported by the experimental data. To our knowledge, it is the first study describing the relation of the timing of S2 to BP by both the model-based study and experimental data. / 3. As a preliminary study, a linear predication model using RS2 with a novel calibration scheme was proposed for BP estimation and it has been evaluated in clinical test on 85 volunteers including 18 hypertensives. The results indicate that the approach has the potential to achieve the accuracy required for medical diagnosis. / Cuffless BP measurement has been proposed as a new concept in recent years to realize the continuous monitoring of BP. This research focuses on the investigation of cuffless BP monitoring technique using heart sound information. Specifically, the thesis proposes a new cuffless technique based on the timing of the second heart sound (S2), which will enable a novel wearable design of BP monitor, for instance, a multifunctional electronic stethoscope. / Finally, based on the findings on both theoretical and experimental studies, a linear prediction model with a novel calibration scheme has been proposed to estimate the BP using 1/RS2. The proposed method was evaluated in a clinical test on 85 volunteers aged 40+/-13 years, including 18 hypertensives. The average of BP measured by simultaneous ausculatory and oscillometric approaches was used as a reference. The results of clinical test shows that the RS2 based approach can estimate SBP and DBP within the 2.1+/-7.4 mmHg and 0.8+/-6.6 mmHg of the reference respectively, indicating the approach has the potential to achieve the accuracy required for medical diagnosis according to AAMI standard (mean error within +/-5mmHg and SD less than 8mmHg) and BHS protocol. / First, a mathematical model has been developed to investigate the effects of aortic BP on the aortic component (A2) in S2 produced by the vibration of the closed aortic valve. The nonlinear elasticity of aortic wall has been introduced to the model, reflecting the nature of aortic wall tissue and extending the model to the applications involving wide BP variations. The results of simulation show that the fundamental frequency and amplitude of A2 increases as aortic systolic blood pressure (SBP) is elevated, which is able to explain the 'accentuated S2' usually heard in the hypertensives. Nevertheless, the possibility of BP measurement using spectral information of externally recorded heart sounds still needs a careful examination because the frequency characteristics tends to be blurred during sound transmission. / Hypertension, known as 'a silent killer', is an important public health challenge, afflicting approximately 1 billion adults around the world. The monitoring of blood pressure (BP) is vitally important in order to identify hypertension and treat it earlier before serious health problems are developed. The conventional BP measurement provides only intermittent BP and causes circulatory interference if the cuff is inflated frequently. There is an urgent need to develop new devices which are fully wearable and unobtrusive for noninvasive and continuous monitoring of arterial BP in daily life. / Second, a modified model of heart-arterial system has been proposed in this thesis for describing the timing of aortic valve closure as a result of heart-arterial interaction. A timing parameter, RS2, was defined as the time delay from the peak of ECG R wave to the onset of S2. The study has investigated the relation between RS2 and aortic BP under varying peripheral resistance, arterial compliance, heart rate, cardiac contractility and preload. Based on the simulation results of parametric analysis, it is hypothesized that RS2 bears a significant negative correlation with both SBP and diastolic blood pressure (DBP) as the peripheral resistance, heart rate or cardiac contractility varies. / Third, in order to verify the findings of the model-based study, three experiments were carried out to explore the relationship between RS2 and BP. The alterations of RS2 in the dynamic-exercise experiments are mainly attributable to the interactive effect of the changes in heart rate, cardiac contractility and peripheral resistance, and the effect of heart rate is dominant. In two dynamic-exercise experiments, the timing parameter, RS2, exhibited a close inverse correlation with SBP (r =0.892 and r =0.845, p<0.05 in both experiments) and a moderate inverse correlation with diastolic blood pressure (DBP) (r = 0.687, p<0.05 and r =0.660, p>0.05). The correlations are comparable to those of PTT-based parameters. However, due to the restricted range of the BP variation, there was no significant correlation observed in long-term rest monitoring experiment. Moreover, the standard deviation (SD) of the errors for SBP and DBP estimated by linear fitting of 1/RS2 is close to that of PTT-based estimation. The results also suggest that the ability of RS2 on BP estimation is as good as that of the PTT based parameters. / To summarize, the original contributions of the thesis are: 1. By the introduction of the nonlinear elasticity of aortic wall, a mathematical model for the vibration of the closed aortic valve was improved and extended to the applications involving wide variations of BP To my knowledge, this represents the first study to look into the effects of aortic BP on the frequency characteristic of S2 from the theoretical point of view. / Zhang Xin-Yu. / "September 2006." / Adviser: Yuan-Ting Zhang. / Source: Dissertation Abstracts International, Volume: 68-09, Section: B, page: 6125. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references. / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

More than one million fetal deaths occur in the United States every year [1]. Monitoring the long-term heart rate variability provides a great amount of information about the fetal health condition which requires continuous monitoring of the fetal heart rate. All the existing technologies have either complex instrumentation or need a trained professional at all times or both. The existing technologies are proven to be impractical for continuous monitoring [2]. Hence, there is an increased interest towards noninvasive, continuous monitoring, and less expensive technologies like fetal phonocardiography. Fetal Phonocardiography (FPCG) signal is obtained by placing an acoustic transducer on the abdomen of the mother. FPCG is rich in physiological bio-signals and can continuously monitor the fetal heart rate non-invasively. Despite its high diagnostic potential, it is still not being used as the secondary point of care. There are two challenges as to why it is still being considered as the secondary point of care; in the data acquisition system and the signal processing methodologies. The challenges pertaining to data acquisition systems are but not limited to sensor placement, maternal obesity and multiple heart rates. While, the challenges in the signal processing methodologies are dynamic nature of FPCG signal, multiple known and unknown signal components and SNR of the signal. Hence, to improve the FPCG based care, challenges in FPCG signal processing methodologies have been addressed in this study. A comparative evaluation was presented on various advanced signal processing techniques to extract the bio-signals with fidelity. Advanced signal processing approaches, namely empirical mode decomposition, spectral subtraction, wavelet decomposition and adaptive filtering were used to extract the vital bio-signals. However, extracting these bio-signals with fidelity is a challenging task in the context of FPCG as all the bio signals and the unwanted artifacts overlap in both time and frequency. Additionally, the signal is corrupted by noise induced from the fetal and maternal movements as well the background and the sensor. Empirical mode decomposition algorithm was efficient to denoise and extract the maternal and fetal heart sounds in a single step. Whereas, spectral subtraction was used to denoise the signal which was later subjected to wavelet decomposition to extract the signal of interest. On the other hand, adaptive filtering was used to estimate the fetal heart sound from a noisy FPCG where maternal heart sound was the reference input. The extracted signals were validated by obtaining the frequency ranges computed by the Short Time Fourier Transform (STFT). It was observed that the bandwidths of extracted fetal heart sounds and maternal heart sounds were consistent with the existing gold standards. Furthermore, as a means of additional validation, the heart rates were calculated. Finally, the results obtained from all these methods were compared and contrasted qualitatively and quantitatively.

Because the voltage amplitude of a heart beat is small compared to the amplitude of exponential noise, pacemakers have difficulty registering the responding heart beat immediately after a pacing pulse. This thesis investigates use of digital filters, an inverse filter and a lowpass filter, to eliminate the effects of exponential noise following a pace pulse. The goal was to create a filter which makes recognition of a haversine wave less dependent on natural subsidence of exponential noise. Research included the design of heart system, pacemaker, pulse generation, and D sensor system simulations. The simulation model includes the following components: \ • Signal source, A MA TLAB generated combination of a haversine signal, exponential noise, and myopotential noise. The haversine signal is a test signal used to simulate the QRS complex which is normally recorded on an ECG trace as a representa tion of heart function. The amplitude is approximately 10 mV. Simulated myopotential noise represents a uniformly distributed random noise which is generated by skeletal muscle tissue. The myopotential noise has a frequency spectrum extending from 70 to 1000Hz. The amplitude varies from 2 to 5 mV. Simulated exponential noise represents the depolarization effects of a pacing pulse as seen at the active cardiac lead. The amplitude is about -1 volt, large in comparison with the haversine signal. • AID converter, A combination of sample & hold and quantizer functions translate the analog signal into a digital signal. Additionally, random noise is created during quantization. • Digital filters, An inverse filter removes the exponential noise, and a lowpass filter removes myopotential noise. • Threshold level detector, A function which detects the strength and amplitude of the output signal was created for robustness and as a data sampling device. The simulation program is written for operation in a DOS environment. The program generates a haversine signal, myopotential noise (random noise), and exponential noise. The signals are amplified and sent to an AID converter stage. The resultant digital signal is sent to a series of digital filters, where exponential noise is removed by an inverse digital filter, and myopotential noise is removed by the Chebyshev type I lowpass digital filter. The output signal is "detected" if its waveform exceeds the noise threshold level. To determine what kind of digital filter would remove exponential noise, the spectrum of exponential noise relative to a haversine signal was examined. The spectrum of the exponential noise is continuous because the pace pulse is considered a non-periodic signal (assuming the haversine signal occurs immediately after a pace pulse). The spectrum of the haversine is also continuous, existing at every value of frequency co. The spectrum of the haversine is overlapped by the spectrum of and amplitude of the exponential, which is several orders of magnitude larger. The exponential cannot be removed by conventional filters. Therefore, an inverse filter approach is used to remove exponential noise. The transfer function of the inverse filter of the model has only zeros. This type of filter is called FIR, all-zero, non recursive, or moving average. Tests were run using the model to investigate the behavior of the inverse filter. It was found that the haversine signal could be clearly detected within a 5% change in the time constant of the exponential noise. Between 5% and 15% of change in the time constant, the filtered exponential amplitude swamps the haversine signal. The sensitivity of the inverse filter was also studied: when using a fixed exponential time constant but changing the location of the transfer function, the effect of the exponential noise on the haversine is minimal when zeros are located between 0.75 and 0.85 of the unit circle. After the source signal passes the inverse filter, the signal consists only of the haversine signal, myopotential noise, and some random noise introduced during quantization. To remove these noises, a Chebyshev type I lowpass filter is used.

There has been in the past and still is controversy over the genesis of the third heart sound (S3). Recent studies, strongly suggest that S3 is a manifestation of a sudden intrinsic limitation in the expansion of the left ventricle. The thesis has aimed to explore that hypothesis further using combined echocardiographic and spectral analysis techniques. Spectral analysis was carried out via conventional fast fourier transform methods and the maximum entropy method. The efficacy of these techniques, in relation to clinical and scientific application, was explored further. Briefly discussed was the application of autoregressive-moving average (ARMA) modelling for spectral analysis of S3, in relation to further work. Following is a brief synopsis of the thesis: CHAPTER 1 This gives an historical and general introduction to heart sound analysis. Discussed briefly is the physiology of the heart and heart sounds and the diagnostic implications of S3 analysis. CHAPTER 2 Here is discussed the instrumentation system used and phonocardiographic and echocardiographic data aquisition. Data preprocessing and storage is also covered. CHAPTER 3 In this chapter the application of a FFT method and correlation of resultant spectral parameters with echocardiographic parameters is reported. CHAPTER 4 The theoretical development of the maximum entropy technique (based on published papers and expanded) is discussed here. Numerical experiments with the method and associated problems are also discussed. CHAPTER 5 The MEM is applied to the spectral analysis of S3 and compared with the FFT method. Correlation analysis of MEM derived spectral parameters with echocardiograhic data is performed. CHAPTER 6 Here ARMA modelling and application to further work is discussed. An ARMA model from the maxixum entropy coefficients is derived. The application of this model to the deconvolution of the chest wall transfer function is discussed as an approach for further work. / Thesis (M.Sc.)--School of Mathematical Sciences, 1989.

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (leaves 275-293).

There has been in the past and still is controversy over the genesis of the third heart sound (S3). Recent studies, strongly suggest that S3 is a manifestation of a sudden intrinsic limitation in the expansion of the left ventricle. The thesis has aimed to explore that hypothesis further using combined echocardiographic and spectral analysis techniques. Spectral analysis was carried out via conventional fast fourier transform methods and the maximum entropy method. The efficacy of these techniques, in relation to clinical and scientific application, was explored further. Briefly discussed was the application of autoregressive-moving average (ARMA) modelling for spectral analysis of S3, in relation to further work. Following is a brief synopsis of the thesis: CHAPTER 1 This gives an historical and general introduction to heart sound analysis. Discussed briefly is the physiology of the heart and heart sounds and the diagnostic implications of S3 analysis. CHAPTER 2 Here is discussed the instrumentation system used and phonocardiographic and echocardiographic data aquisition. Data preprocessing and storage is also covered. CHAPTER 3 In this chapter the application of a FFT method and correlation of resultant spectral parameters with echocardiographic parameters is reported. CHAPTER 4 The theoretical development of the maximum entropy technique (based on published papers and expanded) is discussed here. Numerical experiments with the method and associated problems are also discussed. CHAPTER 5 The MEM is applied to the spectral analysis of S3 and compared with the FFT method. Correlation analysis of MEM derived spectral parameters with echocardiograhic data is performed. CHAPTER 6 Here ARMA modelling and application to further work is discussed. An ARMA model from the maxixum entropy coefficients is derived. The application of this model to the deconvolution of the chest wall transfer function is discussed as an approach for further work. / Thesis (M.Sc.)--School of Mathematical Sciences, 1989.

Made available in DSpace on 2014-12-02T11:16:55Z (GMT). No. of bitstreams: 0 Previous issue date: 2014-02-14Bitstream added on 2014-12-02T11:20:54Z : No. of bitstreams: 1 000796411.pdf: 2346587 bytes, checksum: 4c090312f84027a406b4735ddd531092 (MD5) / A presente dissertação teve como objetivo apresentar uma proposta de análise de bulhas cardíacas (sons produzidos pelo fechamento das válvulas do coração) usando Transformada Discreta de Wavelet. Neste trabalho as bulhas cardíacas, gravadas em um arquivo digital, foram processadas através da Transformada Discreta de Wavelet nível 6 da db7 e da db6 de Daubechies e feita uma análise de sua média e do seu desvio padrão. Com a métrica desvio padrão aplicada ao sexto nível da db6 de Daubechies para classificação de sinais normais e anormais em um banco de dados de 70 amostras obteve-se um acerto da ordem de 95,71% / This dissertation aims to present a proposal for interpretation of heart sounds using Discrete Wavelet Transform. The heart sounds recorded in a digital file were processed using level 6 of db7 and level 6 of db6 Daubechies Discrete Wavelet Transform and extracting the media and standard deviation features. The standard deviation of level6 of db6 Daubechies Discrete Wavelet is are able to differentiate between normal and abnormal from database of 70 heart sound signals with 95.71% of correct classifications

Orientador: Nobuo Oki / Banca: Suely Cunha Amaro Mantovani / Banca: Carlos Aurélio Faria da Rocha / Resumo: A presente dissertação teve como objetivo apresentar uma proposta de análise de bulhas cardíacas (sons produzidos pelo fechamento das válvulas do coração) usando Transformada Discreta de Wavelet. Neste trabalho as bulhas cardíacas, gravadas em um arquivo digital, foram processadas através da Transformada Discreta de Wavelet nível 6 da db7 e da db6 de Daubechies e feita uma análise de sua média e do seu desvio padrão. Com a métrica desvio padrão aplicada ao sexto nível da db6 de Daubechies para classificação de sinais normais e anormais em um banco de dados de 70 amostras obteve-se um acerto da ordem de 95,71% / Abstract: This dissertation aims to present a proposal for interpretation of heart sounds using Discrete Wavelet Transform. The heart sounds recorded in a digital file were processed using level 6 of db7 and level 6 of db6 Daubechies Discrete Wavelet Transform and extracting the media and standard deviation features. The standard deviation of level6 of db6 Daubechies Discrete Wavelet is are able to differentiate between normal and abnormal from database of 70 heart sound signals with 95.71% of correct classifications / Mestre

Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2005. / Most of the relevant and severe congenital cardiac malfunctions can be recognized in the neonatal period of a child’s life. The delayed recognition of a congenital heart defect may have a serious impact on the long-term outcome of the affected child. Experienced cardiologists can usually evaluate heart murmurs with a high sensitivity and specificity, although non-specialists, with less clinical experience, may have more difficulty. Although primary care physicians frequently encounter children with heart murmurs most of these murmurs are innocent. The aim of this project is to design an automated algorithm that can assist the primary care physician in screening and diagnosing pediatric patients with possible cardiac malfunctions. Although attempts have been made to automate screening by auscultation, no device is currently available to fulfill this function. Multiple indicators of pathology are nonetheless available from heart sounds and were elicited using several signal processing techniques. The three feature extraction algorithms (FEA’s) developed respectively made use of a Direct Ratio technique, a Wavelet analysis technique and a Knowledge based neural network technique. Several implementations of each technique are evaluated to identify the best performer. To test the performance of the various algorithms, the clinical auscultation sounds and ECG-data of 163 patients, aged between 2 months and 16 years, were digitized. Results presented show that the De-noised Jack-Knife neural network can classify 163 recordings with a sensitivity and specificity of 92 % and 92.9 % respectively. This study concludes that, in certain conditions, the developed automated auscultation algorithms show significant potential in their use as an alternative evaluation technique for the classification of heart sounds in normal (innocent) and pathological classes.

Thesis (MScEng (Mechanical and Mechatronic Engineering))--University of Stellenbosch, 2007. / This thesis is concerned with the testing of an “auscultation jacket” as a means of recording heart sounds and electrocardiography (ECG) data from patients. A classification system based on Neural Networks, that is able to discriminate between normal and abnormal heart sounds and murmurs, has also been developed . The classification system uses the recorded data as training and testing data. This classification system is proposed to serve as an aid to physicians in diagnosing patients with cardiac abnormalities. Seventeen normal participants and 14 participants that suffer from valve-related heart disease have been recorded with the jacket. The “auscultation jacket” shows great promise as a wearable health monitoring aid for application in rural areas and in the telemedicine industry. The Neural Network classification system is able to differentiate between normal and abnormal heart sounds with a sensitivity of 85.7% and a specificity of 94.1%.