Results 41 to 50 of about 5,008 (218)

Deep learning‐based high‐dimensional multiple regression estimator for chest x‐ray image classification in rapid cardiomegaly screening

Translational Biophotonics, Volume 5, Issue 3-4, August-November 2023., 2023
For a chest x‐ray image, we obtain an input map with the bounding box and then feed it to the proposed two‐channel CNPP (convolution‐normalization‐pooling processes) paths to extract the feature parameters of the heart and left/right thoracic cages. In the classification layer, the multilayer network is used to predict the four coordinate points of the
Pi‐Yun Chen, Chia‐Hung Lin, Hung‐Yao Peng, Feng‐Zhou Zhang, Chung‐Dann Kan   +4 more
wiley   +1 more source

Wavelet-based fundamental heart sound recognition method using morphological and interval features

Healthcare Technology Letters, 2018
Accurate and reliable recognition of fundamental heart sounds (FHSs) plays a significant role in automated analysis of heart sound (HS) patterns. This Letter presents an automated wavelet-based FHS recognition (WFHSR) method using morphological and ...
V. Nivitha Varghees, K.I. Ramachandran, K.P. Soman   +2 more
doaj   +1 more source

KLASIFIKASI SINYAL PHONOCARDIOGRAM MENGGUNAKAN METODE SHORTTIME FOURIER TRANSFORM DAN SUPPORT VECTOR MACHINE

Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer, 2023
Penggunaan phonocardiogram (PCG) dalam mengekstraksi informasi-informasi secara elektronik membutuhkan analisis sinyal yang kompleks. Namun, PCG memiliki keunggulan bersifat non invasif dan hemat dibandingkan dengan electrocardiogram (EKG).
Faqih Amatya Hendrayan, Dodi Zulherman, Rahmat Widadi   +2 more
doaj   +1 more source

Vowel classification with combining pitch detection and one‐dimensional convolutional neural network based classifier for gender identification

IET Signal Processing, Volume 17, Issue 5, May 2023., 2023
The YAAPT‐based pitch detection algorithm is used to design an extractor to extract the spectral peak (F0) patterns, and 1D CNN‐based classifier is employed to use the feature pattern to identify females or males in adult or children groups. Abstract Human speech signals may contain specific information regarding a speaker's characteristics, and these ...
Chia‐Hung Lin, Hsiang‐Yueh Lai, Ping‐Tzan Huang, Pi‐Yun Chen, Chien‐Ming Li   +4 more
wiley   +1 more source

Development of a Finger-Ring-Shaped Hybrid Smart Stethoscope for Automatic S1 and S2 Heart Sound Identification

Sensors, 2021
Cardiac auscultation is one of the most popular diagnosis approaches to determine cardiovascular status based on listening to heart sounds with a stethoscope.
Soomin Lee, Qun Wei, Heejoon Park, Yuri Na, Donghwa Jeong, Hongjoon Lim   +5 more
doaj   +1 more source

Algorithm for heart rate extraction in a novel wearable acoustic sensor. [PDF]

, 2015
Phonocardiography is a widely used method of listening to the heart sounds and indicating the presence of cardiac abnormalities. Each heart cycle consists of two major sounds - S1 and S2 - that can be used to determine the heart rate.
Aguilar-Pelaez, E, Chen, G, Imtiaz, SA, Rodriguez-Villegas, E   +3 more
core   +1 more source

Non-invasive Blood Pressure Estimation Using Phonocardiogram [PDF]

2017 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1-4. IEEE, 2017, 2019
This paper presents a novel approach based on pulse transit time (PTT) for the estimation of blood pressure (BP). In order to achieve this goal, a data acquisition hardware is designed for high-resolution sampling of phonocardiogram (PCG) and photoplethysmogram (PPG). These two signals can derive PTT values. Meanwhile, a force-sensing resistor (FSR) is
arxiv   +1 more source

A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation

, 2020
Traditionally, abnormal heart sound classification is framed as a three-stage process. The first stage involves segmenting the phonocardiogram to detect fundamental heart sounds; after which features are extracted and classification is performed.
Denman, Simon, Dissanayake, Theekshana, Fernando, Tharindu, Fookes, Clinton, Ghaemmaghami, Houman, Sridharan, Sridha   +5 more
core   +1 more source

Which Augmentation Should I Use? An Empirical Investigation of Augmentations for Self-Supervised Phonocardiogram Representation Learning [PDF]

, 2023
Despite recent advancements in deep learning, its application in real-world medical settings, such as phonocardiogram (PCG) classification, remains limited. A significant barrier is the lack of high-quality annotated datasets, which hampers the development of robust, generalizable models that can perform well on newly collected, out-of-distribution ...
arxiv   +1 more source

The Heart Defect Analysis Based on PCG Signals Using Pattern Recognition Techniques [PDF]

, 2016
The graphical recording of the heart sounds and murmurs is called Phonocardiogram or PCG and the machine is so called phonocardiograph. It has an important role in the proper and accurate diagnosis of the heart defects. It requires highly and experienced
Lubaib, P., Muneer, K.V. Ahammed
core   +1 more source