Roland Priemer

Bio: Roland Priemer is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Heart sounds & Digital image processing. The author has an hindex of 6, co-authored 10 publications receiving 175 citations.

Accessing heart dynamics to estimate durations of heart sounds

Abstract: Segmentation of the phonocardiogram into its major sound components is the first step in the automated diagnosis of cardiac abnormalities. Almost all of the existing phonocardiogram segmentation algorithms utilize absolute amplitude or frequency characteristics of heart sounds, which vary from one cardiac cycle to the other and across different patients. The objective of this work is to provide an efficient phonocardiogram segmentation technique, under difficult recording situations, by utilizing the underlying complexity of the dynamical system (heart) giving rise to the heart sound. Complexity-based segmentation is invariant to amplitude and frequency variations of the heart sound and yields better time gates for heart sounds.

Introductory Signal Processing

Abstract: A valuable introduction to the fundamentals of continuous and discrete time signal processing, this book is intended for the reader with little or no background in this subject. The emphasis is on development from basic principles. With this book the reader can become knowledgeable about both the theoretical and practical aspects of digital signal processing.Some special features of this book are: (1) gradual and step-by-step development of the mathematics for signal processing, (2) numerous examples and homework problems, (3) evolutionary development of Fourier series, Discrete Fourier Transform, Fourier Transform, Laplace Transform, and Z-Transform, (4) emphasis on the relationship between continuous and discrete time signal processing, (5) many examples of using the computer for applying the theory, (6) computer based assignments to gain practical insight, (7) a set of computer programs to aid the reader in applying the theory.

Extraction of one or more discrete heart sounds from heart sound information

Abstract: A heart sound analyzer component of an apparatus in one example extracts from composite heart sound information one or more discrete heart sounds of one or more corresponding distinct heart sound sources.

A Procedure to Extract the Aortic and the Pulmonary Sounds from the Phonocardiogram

Abstract: The time interval between the aortic (A2) and pulmonary (P2) components of the second heart sound (S2) is an indicator of the presence and severity of several cardiac abnormalities. However, in many cases identification of the A2 and P2 components is difficult due to their temporal overlap and significant spectral similarity. In this work, we present a method to extract the A2 and P2 components from the S2 sound, by assuming their mutual statistical independence. Once extracted, the A2 and P2 components are identified by using a physiological reference signal. Results obtained from real data are encouraging, and show promise for utilizing the proposed method in a clinical setting to non-invasively tract the A2-P2 time interval.

Extraction of heart sound of fetus from heart sound information that comprises plurality of mixtures of plurality of heart sounds of plurality of fetuses

Abstract: A heart sound analyzer component of an apparatus in one example extracts a heart sound of a fetus from heart sound information that comprises a plurality of mixtures of a plurality of heart sounds of a plurality of fetuses.

An open access database for the evaluation of heart sound algorithms.

Abstract: In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced from multiple research groups around the world. It includes 2435 heart sound recordings in total collected from 1297 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. The recordings were collected from a variety of clinical or nonclinical (such as in-home visits) environments and equipment. The length of recording varied from several seconds to several minutes. This article reports detailed information about the subjects/patients including demographics (number, age, gender), recordings (number, location, state and time length), associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a brief summary of the commonly used heart sound segmentation and classification methods, including open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for different heart sound states, the scoring mechanism, and associated open source code are provided. In addition, several potential benefits from the public heart sound database are discussed.

AM-compatible digital broadcasting method and system

Abstract: A method and system are provided for the transmission and reception of a composite radio-frequency (RF) signal including a supplemental signal, preferably representing encoded digital information, together with an analog signal which represents monophonic analog audio in the AM-band. The analog monophonic component of the composite signal may be received by conventional AM-band audio receivers. In certain embodiments, the analog signal is a single-sideband large-carrier or vestigial-sideband large-carrier signal, and the composite RF signal includes a digital signal whose spectrum is substantially confined in one inner sideband. In other embodiments, a baseband digital signal is combined with an analog monophonic audio signal and transmitted in upper inner and lower inner sidebands using nonlinear compatible quadrature amplitude modulation (NC-QAM). Additional digital signals' spectrum occupies the lower outer and upper outer sidebands. In certain embodiments, for each transmitted codeword, part of the codeword information is replicated by modulated signals in both the upper outer and lower outer sidebands, preferably with diversity delay between the outer sideband signals.

Segmentation of heart sound recordings by a duration dependent Hidden Markov Model

Abstract: Digital stethoscopes offer new opportunities for computerized analysis of heart sounds. Segmentation of heart sound recordings into periods related to the first and second heart sound (S1 and S2) is fundamental in the analysis process. However, segmentation of heart sounds recorded with handheld stethoscopes in clinical environments is often complicated by background noise. A duration-dependent hidden Markov model (DHMM) is proposed for robust segmentation of heart sounds. The DHMM identifies the most likely sequence of physiological heart sounds, based on duration of the events, the amplitude of the signal envelope and a predefined model structure. The DHMM model was developed and tested with heart sounds recorded bedside with a commercially available handheld stethoscope from a population of patients referred for coronary arterioangiography. The DHMM identified 890 S1 and S2 sounds out of 901 which corresponds to 98.8% (CI: 97.8-99.3%) sensitivity in 73 test patients and 13 misplaced sounds out of 903 identified sounds which corresponds to 98.6% (CI: 97.6-99.1%) positive predictivity. These results indicate that the DHMM is an appropriate model of the heart cycle and suitable for segmentation of clinically recorded heart sounds.

Segmentation of heart sound recordings by a duration-dependent hidden Markov model

Abstract: Digital stethoscopes offer new opportunities for computerized analysis of heart sounds. Segmentation of heart sound recordings into periods related to the first and second heart sound (S1 and S2) is fundamental in the analysis process. However, segmentation of heart sounds recorded with handheld stethoscopes in clinical environments is often complicated by background noise. A duration-dependent hidden Markov model (DHMM) is proposed for robust segmentation of heart sounds. The DHMM identifies the most likely sequence of physiological heart sounds, based on duration of the events, the amplitude of the signal envelope and a predefined model structure. The DHMM model was developed and tested with heart sounds recorded bedside with a commercially available handheld stethoscope from a population of patients referred for coronary arterioangiography. The DHMM identified 890 S1 and S2 sounds out of 901 which corresponds to 98.8% (CI: 97.8-99.3%) sensitivity in 73 test patients and 13 misplaced sounds out of 903 identified sounds which corresponds to 98.6% (CI: 97.6-99.1%) positive predictivity. These results indicate that the DHMM is an appropriate model of the heart cycle and suitable for segmentation of clinically recorded heart sounds.

The electronic stethoscope

Abstract: Most heart diseases are associated with and reflected by the sounds that the heart produces. Heart auscultation, defined as listening to the heart sound, has been a very important method for the early diagnosis of cardiac dysfunction. Traditional auscultation requires substantial clinical experience and good listening skills. The emergence of the electronic stethoscope has paved the way for a new field of computer-aided auscultation. This article provides an in-depth study of (1) the electronic stethoscope technology, and (2) the methodology for diagnosis of cardiac disorders based on computer-aided auscultation. The paper is based on a comprehensive review of (1) literature articles, (2) market (state-of-the-art) products, and (3) smartphone stethoscope apps. It covers in depth every key component of the computer-aided system with electronic stethoscope, from sensor design, front-end circuitry, denoising algorithm, heart sound segmentation, to the final machine learning techniques. Our intent is to provide an informative and illustrative presentation of the electronic stethoscope, which is valuable and beneficial to academics, researchers and engineers in the technical field, as well as to medical professionals to facilitate its use clinically. The paper provides the technological and medical basis for the development and commercialization of a real-time integrated heart sound detection, acquisition and quantification system.