Exploring the Impact of Noise and Degradations on Heart Sound Classification Models

The development of data-driven heart sound classification models has been an active area of research in recent years. To develop such data-driven models in the first place, heart sound signals need to be captured using a signal acquisition device. However, it is almost impossible to capture noise-free heart sound signals due to the presence of internal and external noises in most situations. Such noises and degradations in heart sound signals can potentially reduce the accuracy of data-driven classification models. Although different techniques have been proposed in the literature to address the noise issue, how and to what extent different noise and degradations in heart sound signals impact the accuracy of data-driven classification models remains unexplored. To answer this question, we produced a synthetic heart sound dataset including normal and abnormal heart sounds contaminated with a variety of noise and degradations. We used this dataset to investigate the impact of noise and degradation in heart sound recordings on the performance of different classification models. The results show different noises and degradations affect the performance of heart sound classification models to a different extent; some are more problematic for classification models, and others are less destructive. Comparing the findings of this study with the results of a survey we previously carried out with a group of clinicians shows noise and degradations that are more detrimental to classification models are also more disruptive to accurate auscultation. The findings of this study can be leveraged to develop targeted heart sound quality enhancement approaches — which adapt the type and aggressiveness of quality enhancement based on the characteristics of noise and degradation in heart sound signals

Systolic ejection murmurs and the left ventricular outflow tract in boxer dogs

Turbulence of various genesis in the left ventricular outflow tract (LVOT) causes systolic ejection murmurs. The prevalence of murmurs in adult boxer dogs is 50-80%, the majority of which are of low intensity. Some of the murmurs are caused by aortic stenosis (AS), while the origin of the others is unclear. The aim of this thesis was to study the physiology and clinical evaluation of systolic ejection murmurs and their relation to the development of the LVOT in boxers with and without AS. Growing and adult boxer dogs were examined by the standard methods cardiac auscultation, ECG, phonocardiography and echocardiography. Additionally, the complementary methods time-frequency and complexity analyses of heart murmurs and contrast echocardiography were evaluated. Studies on inter-observer variation in cardiac auscultation proved the importance of experience in detection and grading of low intensity ejection murmurs. Excitement of the dogs by exercise or noise stimulation (barking dog and squeaky toy) caused higher murmur grades, longer murmur duration and increased aortic flow velocities. No differences were found between diameters measured at different levels of the LVOT in growing boxers. Contrast echocardiography enhanced Doppler signals, but did not allow evaluation of myocardial blood flow. Using time-frequency analysis, duration of murmur frequency >200 Hz proved useful for differentiation between dogs with mild AS and dogs without. Combining assessment of murmur duration >200 Hz and complexity analysis using the correlation dimension (T2), a sensitivity of 94% and a specificity of 82% for differentiation between dogs with and without AS was achieved. The variability in presence and intensity of low intensity murmurs during growth was high. None of the young dogs developed AS, whereas 3 out of 16 individuals developed mild-moderate aortic insufficiency. Aortic or pulmonic flow velocities did not differ significantly between growing dogs with or without low intensity murmurs. In conclusion, the variability in presence and intensity of low intensity ejection murmurs in boxers is high during growth with no obvious progression. Both in young and adult boxers the murmur grade increased during excitement, which may be due to rapid flow in a comparatively small LVOT that has been suggested for the boxer breed. Experience is important in cardiac auscultation of low intensity murmurs. Therefore, assessment of murmur duration > 200 Hz combined with T2 analysis may be a useful complementary method for diagnosis of cardiovascular function in dogs

Diagnosis of heart valve disorders through trapezoidal features and hybrid classifier.

Numerous studies are being conducted in recent years focusing on phonocardiographic (PCG) signals due to their capability to characterize heart sounds. These characteristics can be exploited in developing computer-aided auscultation system as a complementary tool for clinicians in diagnosis of cardiovascular disorders. This study proposes a new type of features to distinguish five categories of heart sounds, including normal, mitral stenosis, mitral regurgitation,aortic stenosis, and aortic regurgitation. PCG signals were collected from online resources and training CDs. Wavelet packet transform was utilized for heart sound analysis as opposed to discrete wavelet transform that has been extensively used in the previous studies. Then, trapezoidal function was calculated for deriving feature vectors. A hybrid classifier was designed composing of three types of classifiers, multilayer perceptron (MLP) artificial neural network, k-nearest neighbor (KNN), and support vector machine (SVM), to classify feature vectors.The promising results demonstrate the effectiveness of the proposed trapezoidal features and hybrid classifier for heart sound classification

Deep Neural Networks for the Recognition and Classification of Heart Murmurs Using Neuromorphic Auditory Sensors

Auscultation is one of the most used techniques for detecting cardiovascular diseases, which is one of the main causes of death in the world. Heart murmurs are the most common abnormal finding when a patient visits the physician for auscultation. These heart sounds can either be innocent, which are harmless, or abnormal, which may be a sign of a more serious heart condition. However, the accuracy rate of primary care physicians and expert cardiologists when auscultating is not good enough to avoid most of both type-I (healthy patients are sent for echocardiogram) and type-II (pathological patients are sent home without medication or treatment) errors made. In this paper, the authors present a novel convolutional neural network based tool for classifying between healthy people and pathological patients using a neuromorphic auditory sensor for FPGA that is able to decompose the audio into frequency bands in real time. For this purpose, different networks have been trained with the heart murmur information contained in heart sound recordings obtained from nine different heart sound databases sourced from multiple research groups. These samples are segmented and preprocessed using the neuromorphic auditory sensor to decompose their audio information into frequency bands and, after that, sonogram images with the same size are generated. These images have been used to train and test different convolutional neural network architectures. The best results have been obtained with a modified version of the AlexNet model, achieving 97% accuracy (specificity: 95.12%, sensitivity: 93.20%, PhysioNet/CinC Challenge 2016 score: 0.9416). This tool could aid cardiologists and primary care physicians in the auscultation process, improving the decision making task and reducing type-I and type-II errors.Ministerio de Economía y Competitividad TEC2016-77785-

Reduksi Suara Menelan dari Rekaman Suara Jantung dengan Metode Root Mean Square

ABSTRAKSI: Auskultasi merupakan teknik mendengarkan suara yang dihasilkan dari proses biologis yang terjadi dalam tubuh. Teknik ini biasanya menggunakan stetoskop sebagai alat bantu. Auskultasi merupakan teknik dasar dalam pemeriksaan kesehatan pasien, dengan stetoskop, dokter mendengarkan suara jantung untuk menentukan kesehatan pasien. Dalam proses auskultasi ini banyak sekali terdengar noise. Salah satu noise tersebut adalah noise suara menelan. Agar hasil pemeriksaan lebih akurat, maka diperlukan rekaman suara jantung yang terbebas dari noise suara menelan.Pada Tugas Akhir ini dilakukan perekaman suara jantung menggunakan stetoskop elektronik dengan format rekaman dalan *.wav, frekuensi sampling 8000 Hz dan durasi 10 detik. Perekaman dilakukan sebanyak tiga kali, yaitu perekaman untuk data latih jantung, data latih menelan dan perekaman data uji yang merupakan rekaman suara jantung yang bercampur dengan suara menelan. Sinyal yang telah di rekam ini kemudian dilakukan proses pre-processing untuk mendapatkan level yang sama pada setiap sinyal. Setelah dilakukan Pre-processing sinyal-sinyal tersebut kemudian di blok menjadi beberapa frame sebelum dilakukan proses ekstraksi ciri.Metode ekstraksi ciri yang digunakan pada tugas akhir ini yaitu dengan metode Root Mean Square dan menghitung nilai Power Average-nya. Setelah ciri sinyal suara jantung dan sinyal suara menelan diketahui maka proses selanjutnya yaitu pengklasifikasian sinyal suara jantung yang bercampur dengan sinyal suara menelan. Klasifikasi sinyal dilakukan dengan metode K-Nearest Neighbor dengan nilai k yang menghasilkan MSE terbaik yaitu k=1, yang berarti hanya 1 titik latih terdekat dengan titik uji yang dijadikan acuan. MSE terbaik yang dihasilkan oleh sistem ini yaitu sebesar 0.0337638. Dengan nilai MSE yang kecil ini, berarti sinyal suara campuran dapat dipisahkan dan menghasilkan sinyal suara jantung yang akurat.Kata Kunci : Auskultasi, Suara Jantung, Root Mean Square (RMS), Power Average, K-NN KlasifikasiABSTRACT: Auscultation is a technique to listen to the sound produced from biological processes that occur in the body. This technique usually uses a stethoscope as a tool. Auscultation is a basic technique in medical examinations of patients, with a stethoscopes, the doctor listened to the voice of the heart to determine the health of patients. In the process of auscultation sounds was a lot of noise. One of these noise is swallowing sound. To be more accurate examination, it is necessary to record the heart sounds, which is free from noise swallowing sound.In the Final is done recording heart sounds using an electronic stethoscope with a role in *. wav format recording, sampling frequency 8000 Hz and a duration of 10 seconds. Recording performed three times, namely for data recording cardiac practice, practice swallowing and data recording of test data which is the heart sound recordings that sound mixed with swallowing. The signal has been recorded is then carried out the pre-processing to obtain the same level on each signal. After Pre-processing of signals later in the block into several frames before the feature extraction process.Feature extraction method used in this thesis is the method of Root Mean Square and calculate value of Average Power. After a characteristic heart sound signals and sound signals are known to swallow the next process is the classification of heart sound signals are mixed with swallowing sound signals. Signal classification is done by K-Nearest Neighbor method with a value of k that produces the best MSE namely k = 1, meaning only 1 point nearest train with test points referenced. The best MSE generated by this system is equal to 0.0337638. With this small value of MSE, mean mixed sound signals can be separated and generate accurate heart sound signals.Keyword: Auscultation, Cardiac Sound, Root Mean Square (RMS), Power Average, K-NN classif

Different Techniques and Algorithms for Biomedical Signal Processing

This paper is intended to give a broad overview of the complex area of biomedical and their use in signal processing. It contains sufficient theoretical materials to provide some understanding of the techniques involved for the researcher in the field. This paper consists of two parts: feature extraction and pattern recognition. The first part provides a basic understanding as to how the time domain signal of patient are converted to the frequency domain for analysis. The second part provides basic for understanding the theoretical and practical approaches to the development of neural network models and their implementation in modeling biological syste

Time-Frequency Analysis Of Heart Sounds Using Windowed And Smooth Windowed Wigner-Ville Distribution

Heart sounds and murmurs are time-varying signals that would best be analyzed using time-frequency analysis. Windowed Wigner-Ville distribution (WWVD) and smooth windowed Wigner-Ville distribution (SWWVD) are used to obtain the timefiequency representation (TFR) of the signal. Determination of parameter setting of WWVD and SWWVD will eliminate the cross-terms and improve TFR. The accuracy of TFR will be determined based on the maiulohe width and signal-to-interference ratio. It is found that the most accurate TFR can be achieved using SWWVD

Convolutional neural network for breathing phase detection in lung sounds

We applied deep learning to create an algorithm for breathing phase detection in lung sound recordings, and we compared the breathing phases detected by the algorithm and manually annotated by two experienced lung sound researchers. Our algorithm uses a convolutional neural network with spectrograms as the features, removing the need to specify features explicitly. We trained and evaluated the algorithm using three subsets that are larger than previously seen in the literature. We evaluated the performance of the method using two methods. First, discrete count of agreed breathing phases (using 50% overlap between a pair of boxes), shows a mean agreement with lung sound experts of 97% for inspiration and 87% for expiration. Second, the fraction of time of agreement (in seconds) gives higher pseudo-kappa values for inspiration (0.73-0.88) than expiration (0.63-0.84), showing an average sensitivity of 97% and an average specificity of 84%. With both evaluation methods, the agreement between the annotators and the algorithm shows human level performance for the algorithm. The developed algorithm is valid for detecting breathing phases in lung sound recordings