A METHOD FOR DIGITAL SIGNAL PROCESSING OF BIOMEDICAL SIGNALS

Abstract

ABSTRACT A METHOD FOR DIGITAL SIGNAL PROCESSING OF BIOMEDICAL SIGNALS The present invention provides a method for digital signal processing of biomedical signals using machine learning techniques. The method comprises signal preprocessing, feature extraction, and classification steps. The method improves the accuracy and reduces the computational complexity of digital signal processing of biomedical signals, making it suitable for real-time analysis.

Specification

Description:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003
Complete Specification
(See section 10 and rule 13)



1.Title of the Invention : A METHOD FOR DIGITAL SIGNAL PROCESSING OF BIOMEDICAL SIGNALS


2. Applicant Name : Dr.M.G.R Educational and Research
Institute

Nationality : Indian

Address : Periyar E.V.R High Road, Maduravoyal, Chennai 600095, Tamil Nadu


3. Preamble to the Description :

The following specification particularly describes the invention and the manner in which it is to be performed.


4. DESCRIPTION
FIELD OF THE INVENTION
The present invention relates to the field of digital signal processing of biomedical signals, specifically for the analysis and interpretation of various types of biomedical signals, including but not limited to electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG), and other physiological signals.
BACKGROUND OF THE INVENTION
The analysis of biomedical signals is crucial in the diagnosis and treatment of many medical conditions. The traditional method of signal processing involves manual interpretation, which is prone to error and is time-consuming. Digital signal processing has emerged as a viable alternative, providing accurate and reliable analysis of biomedical signals. However, current methods for digital signal processing of biomedical signals have limitations, such as low accuracy, high computational complexity, and limited ability to handle noisy signals.

SUMMARY OF THE INVENTION
The present invention provides a method for digital signal processing of biomedical signals that addresses the limitations of current methods. The method comprises several steps, including signal preprocessing, feature extraction, and classification. The method uses machine learning techniques to improve accuracy and reduce computational complexity, making it suitable for real-time analysis of biomedical signals.

DETAILED DESCRIPTION OF THE INVENTION
The method for digital signal processing of biomedical signals comprises the following steps:
Step 1: Signal Preprocessing
The first step in the method is signal preprocessing, which is essential for removing any noise and artifacts that may be present in the biomedical signal. The preprocessing step includes filtering, baseline removal, and artifact removal techniques. Filtering is used to remove unwanted frequencies from the signal, while baseline removal techniques remove the baseline drift that may be present in the signal. Artifact removal techniques are used to remove any extraneous signals that may be present in the signal, such as muscle activity or movement artifacts.
Step 2: Feature Extraction
After preprocessing, the next step is feature extraction. In this step, relevant features are extracted from the preprocessed signal. The features are selected based on their ability to represent the underlying physiological phenomena. The feature extraction step includes time-domain and frequency-domain analysis, wavelet analysis, and other relevant techniques.
In time-domain analysis, features such as amplitude, duration, and slope are extracted from the signal. In frequency-domain analysis, features such as power spectral density, frequency band power, and coherence are extracted from the signal. Wavelet analysis is a technique that decomposes the signal into different frequency bands, allowing for the extraction of features at multiple scales.
Step 3: Classification
The final step in the method is classification, where the extracted features are used to classify the biomedical signal. The classification step uses machine learning algorithms to identify the specific physiological condition or disease state. The classification step includes supervised and unsupervised learning algorithms, such as support vector machines (SVM), neural networks, and clustering algorithms.
In supervised learning, the algorithm is trained on a dataset of labeled examples, where the correct classification is known. The algorithm then uses this training data to classify new, unlabeled data. In unsupervised learning, the algorithm is not given any labeled examples and must identify patterns in the data on its own. Clustering algorithms are an example of unsupervised learning and are used to group similar signals together.
The method for digital signal processing of biomedical signals described here improves the accuracy and reduces the computational complexity of traditional signal processing methods. By using machine learning techniques, the method can handle noisy signals and is suitable for real-time analysis of biomedical signals. The method can be applied to a wide range of biomedical signals, including electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG), and other physiological signals.
 












, Claims:CLAIMS
We Claim:
1. A method for digital signal processing of biomedical signals, comprising the steps of signal preprocessing, feature extraction, and classification.
2. The method of claim 1, wherein the signal preprocessing step includes filtering, baseline removal, and artifact removal techniques.
3. The method of claim 1, wherein the feature extraction step includes time-domain and frequency-domain analysis, wavelet analysis, and other relevant techniques.
4. The method of claim 1, wherein the classification step includes supervised and unsupervised learning algorithms, such as support vector machines (SVM), neural networks, and clustering algorithms.
5. The method of claim 1, wherein the method is used for real-time analysis of biomedical signals.
Dated this the 16th November 2024

Digitally signed
Senthil Kumar B
Agent for the applicant
IN/PA-1549

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