Heart Sound Abnormality Detection using Wavelet Packet Features and Machine Learning

2021 ◽  
Author(s):  
Rohith Sai V ◽  
Biswajit Karan ◽  
Garima Thakur ◽  
Ashutosh Rath ◽  
Sitanshu Sekhar Sahu
Keyword(s):  
Machine Learning ◽  
Heart Sound ◽  
Wavelet Packet ◽  
Abnormality Detection

COMPARISON BETWEEN DISCRETE AND PACKET WAVELET TRANSFORM ANALYSES IN THE STUDY OF HEARTBEAT CARDIAC SOUNDS

2007 ◽  
Vol 07 (02) ◽  
pp. 199-214 ◽  
Author(s):  
S. M. DEBBAL ◽  
F. BEREKSI-REGUIG
Keyword(s):  
Wavelet Transform ◽  
Heart Sound ◽  
Wavelet Packet ◽  
Extraction Methods ◽  
Heart Sounds ◽  
Discrete Wavelet ◽  
Clinical Tool ◽  
Prognostic Information ◽  
Transform Method ◽  
Time Frequency

This work investigates the study of heartbeat cardiac sounds through time–frequency analysis by using the wavelet transform method. Heart sounds can be utilized more efficiently by medical doctors when they are displayed visually rather through a conventional stethoscope. Heart sounds provide clinicians with valuable diagnostic and prognostic information. Although heart sound analysis by auscultation is convenient as a clinical tool, heart sound signals are so complex and nonstationary that they are very difficult to analyze in the time or frequency domain. We have studied the extraction of features from heart sounds in the time–frequency (TF) domain for the recognition of heart sounds through TF analysis. The application of wavelet transform (WT) for heart sounds is thus described. The performances of discrete wavelet transform (DWT) and wavelet packet transform (WP) are discussed in this paper. After these transformations, we can compare normal and abnormal heart sounds to verify the clinical usefulness of our extraction methods for the recognition of heart sounds.

scholarly journals Tool wear estimation in turning of Inconel 718 based on wavelet sensor signal analysis and machine learning paradigms

2020 ◽  
Vol 14 (5-6) ◽  
pp. 693-705
Author(s):  
Tiziana Segreto ◽  
Doriana D’Addona ◽  
Roberto Teti
Keyword(s):  
Machine Learning ◽  
Tool Wear ◽  
Inconel 718 ◽  
Signal Analysis ◽  
Elevated Temperatures ◽  
Wavelet Packet ◽  
Research Work ◽  
High Strength ◽  
Signal Features ◽  
Tool Wear Estimation

AbstractIn the last years, hard-to-machine nickel-based alloys have been widely employed in the aerospace industry for their properties of high strength, excellent resistance to corrosion and oxidation, and long creep life at elevated temperatures. As the machinability of these materials is quite low due to high cutting forces, high temperature development and strong work hardening, during machining the cutting tool conditions tend to rapidly deteriorate. Thus, tool health monitoring systems are highly desired to improve tool life and increase productivity. This research work focuses on tool wear estimation during turning of Inconel 718 using wavelet packet transform (WPT) signal analysis and machine learning paradigms. A multiple sensor monitoring system, based on the detection of cutting force, acoustic emission and vibration acceleration signals, was employed during experimental turning trials. The detected sensor signals were subjected to WPT decomposition to extract diverse signal features. The most relevant features were then selected, using correlation measurements, in order to be utilized in artificial neural network based machine learning paradigms for tool wear estimation.

Fuzzy Ontology Based Activity Recognition for Assistive Health Care Using Smart Home

2020 ◽  
Vol 16 (1) ◽  
pp. 17-31 ◽  
Author(s):  
K.S. Gayathri ◽  
K.S. Easwarakumar ◽  
Susan Elias
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Activity Recognition ◽  
Smart Home ◽  
Elderly Care ◽  
Supervised Machine Learning ◽  
Abnormality Detection ◽  
Activity Model ◽  
Pattern Clustering ◽  
Fuzzy Ontology

Assistive health care system is a viable solution for elderly care to offer independent living. Such health care systems are feasible through smart homes, which are intended to enhance the living quality of the occupant. Activities of daily living (ADL) are considered in the design of a smart home and are extended to abnormality detection in the case of health care. Abnormality in occupant behavior is the deviation of ongoing activity with that of the built activity model. Generally, supervised machine learning strategies or knowledge engineering strategies are employed in the process of activity modeling. Supervised machine learning approaches incur overheads in annotating the dataset, while the knowledge modeling approaches incur overhead by being dependent on the domain expert for occupant specific knowledge. The proposed approach on the other hand, employs an unsupervised machine learning strategy to readily extract knowledge from unlabelled data using contextual pattern clustering and subsequently represents it as ontology activity model. Ontology offers enhanced activity recognition through its semantically clear representation and reasoning, it has restriction in handling temporal data. Hence, this article in addition to unsupervised modeling focuses at enabling temporal reasoning within ontology using fuzzy logic. The proposed fuzzy ontology activity recognition (FOAR) framework represents an activity model as a fuzzy temporal ontology. Fuzzy SWRL rules modeled within ontology aid activity recognition and abnormality detection for health care. The experimental results show that the proposed FOAR has better performance in abnormality detection than that of the existing systems.

scholarly journals Multi-level basis selection of wavelet packet decomposition tree for heart sound classification

2013 ◽  
Vol 43 (10) ◽  
pp. 1407-1414 ◽  
Author(s):  
Fatemeh Safara ◽  
Shyamala Doraisamy ◽  
Azreen Azman ◽  
Azrul Jantan ◽  
Asri Ranga Abdullah Ramaiah
Keyword(s):  
Heart Sound ◽  
Wavelet Packet ◽  
Wavelet Packet Decomposition ◽  
Decomposition Tree ◽  
Sound Classification ◽  
Multi Level ◽  
Selection Of

scholarly journals Heartbeat Abnormality Detection using Machine Learning Models and Rate Variability (HRV) Data

2018 ◽  
Author(s):  
Pratik Vyas ◽  
Diptangshu Pandit
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Machine Learning Techniques ◽  
Data Series ◽  
Abnormality Detection ◽  
Learning Models ◽  
Heart Rate Data ◽  
Learning Techniques ◽  
Minimal Data ◽  
Machine Learning Models

The use of machine learning techniques in predictive health care is on the rise with minimal data used for training machine-learning models to derive high accuracy predictions. In this paper, we propose such a system, which utilizes Heart Rate Variability (HRV) as features for training machine learning models. This paper further benchmarks the usefulness of HRV as features calculated from basic heart-rate data using a window shifting method. The benchmarking has been conducted using different machine-learning classifiers such as artificial neural network, decision tree, k-nearest neighbour and naive bays classifier. Empirical results using MIT-BIH Arrhythmia database shows that the proposed system can be used for highly efficient predictability of abnormality in heartbeat data series.

scholarly journals Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261040
Author(s):  
Zazilah May ◽  
M. K. Alam ◽  
Nazrul Anuar Nayan ◽  
Noor A’in A. Rahman ◽  
Muhammad Shazwan Mahmud
Keyword(s):  
Machine Learning ◽  
Acoustic Emission ◽  
Feature Extraction ◽  
Wavelet Packet ◽  
Support Vector ◽  
Learning Approach ◽  
Severity Assessment ◽  
Corrosion Detection ◽  
Support Vector Classifier ◽  
Machine Learning Approach

Corrosion in carbon-steel pipelines leads to failure, which is a major cause of breakdown maintenance in the oil and gas industries. The acoustic emission (AE) signal is a reliable method for corrosion detection and classification in the modern Structural Health Monitoring (SHM) system. The efficiency of this system in detection and classification mainly depends on the suitable AE features. Therefore, many feature extraction and classification methods have been developed for corrosion detection and severity assessment. However, the extraction of appropriate AE features and classification of various levels of corrosion utilizing these extracted features are still challenging issues. To overcome these issues, this article proposes a hybrid machine learning approach that combines Wavelet Packet Transform (WPT) integrated with Fast Fourier Transform (FFT) for multiresolution feature extraction and Linear Support Vector Classifier (L-SVC) for predicting corrosion severity levels. A Laboratory-based Linear Polarization Resistance (LPR) test was performed on carbon-steel samples for AE data acquisition over a different time span. AE signals were collected at a high sampling rate with a sound well AE sensor using AEWin software. Simulation results show a linear relationship between the proposed approach-based extracted AE features and the corrosion process. For multi-class problems, three corrosion severity stages have been made based on the corrosion rate over time and AE activity. The ANOVA test results indicate the significance within and between the feature-groups where F-values (F-value>1) rejects the null hypothesis and P-values (P-value<0.05) are less than the significance level. The utilized L-SVC classifier achieves higher prediction accuracy of 99.0% than the accuracy of other benchmarked classifiers. Findings of our proposed machine learning approach confirm that it can be effectively utilized for corrosion detection and severity assessment in SHM applications.

scholarly journals A Machine-Learning Approach Combining Wavelet Packet Denoising with Catboost for Weather Forecasting

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1618
Author(s):  
Dan Niu ◽  
Li Diao ◽  
Zengliang Zang ◽  
Hongshu Che ◽  
Tianbao Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Weather Forecasting ◽  
Human Life ◽  
Wavelet Packet ◽  
Weather Prediction ◽  
Convergence Time ◽  
Observation Data ◽  
Initial State ◽  
Historical Observation ◽  
Meteorological Elements

Accurate forecasting of future meteorological elements is critical and has profoundly affected human life in many aspects from rainstorm warning to flight safety. The conventional numerical weather prediction (NWP) sometimes leads to unsatisfactory performance due to inappropriate initial state settings. In this paper, a short-term weather forecasting model based on wavelet packet denoising and Catboost is proposed, which takes advantage of the fusion information combining the historical observation data with the prior knowledge from NWP. The feature selection and spatiotemporal feather addition are also explored to further improve performance. The proposed method is evaluated on the datasets provided by Beijing weather stations. Experimental results demonstrate that compared with many deep-learning or machine-learning methods such as LSTM, Seq2Seq, and random forest, the proposed Catboost model incorporated with wavelet packet denoising can achieve shorter convergence time and higher prediction accuracy.

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