Analysis of MFCC Features for Eeg Signal Classification

D, Gnana Rajesh

doi:10.29284/ijasis.2.2.2016.14-20

Cited by 9 publications

(3 citation statements)

References 11 publications

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“…In this case, MFCC features can be used to represent these features and help in the prediction of BGL. MFCC features are already used in [35][36][37][38] to extract features from physiological signals for use in multiple applications. We extracted 24 MFCC features using a window length of 512, an overlap length of 200 and a filter bank with a frequency range from 1.5 Hz to 2 Hz.…”

Section: Ppg Datasetmentioning

confidence: 99%

QU-GM: An IoT Based Glucose Monitoring System From Photoplethysmography, Blood Pressure, and Demographic Data Using Machine Learning

Nazmul Islam Shuzan,

Hossain Chowdhury,

Chowdhury

et al. 2024

IEEE Access

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Patients with hyperglycemia require routine glucose monitoring to effectively treat their condition. We have developed a lightweight wristband device to capture Photoplethysmography (PPG) signals. We collected PPG signals, demographic information, and blood pressure data from 139 diabetic (49.65%) and non-diabetic (50.35%) subjects. Blood glucose was estimated, and diabetic severity (normal, warning, and dangerous) was stratified using Mel frequency cepstral coefficients, time, frequency, and statistical features from PPG and their derivative signals along with physiological parameters. Bagged Ensemble Trees outperform other algorithms in estimating blood glucose level with a correlation coefficient of 0.90. The proposed model's prediction was all in Zone A and B in the Clarke Error Grid analysis. The predictions are thus clinically acceptable. Furthermore, K-nearest neighbor model classified the severity levels with an accuracy of 98.12%. Furthermore, the proposed models were deployed in Amazon Web Server. The wristband is connected to an Android mobile application to collect real-time data and update the estimated glucose and diabetic severity every 10-seconds, which will allow the users to gain better control of their diabetic health.

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Section: Ppg Datasetmentioning

confidence: 99%

QU-GM: An IoT Based Glucose Monitoring System From Photoplethysmography, Blood Pressure, and Demographic Data Using Machine Learning

Nazmul Islam Shuzan,

Hossain Chowdhury,

Chowdhury

et al. 2024

IEEE Access

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“…In essence, LFCCs are representing the spectral envelope of a signal using a set of coefficients. Due to its non-linear frequency scale, de-correlated makeup, and noise resistance, this representation is frequently utilized in signal processing [31]. Signals behave as quasi-stationary in short periods; hence a small window size should be employed while analysing LFCC features frame by frame.…”

Section: A Linear Frequency Cepstral Coefficientsmentioning

confidence: 99%

Autism Classification and Identification of Significant Brain Lobe Using Cepstral Coefficients

Shanmugam,

Radhakrishnan

2024

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Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by restricted, repetitive behaviors and impaired social interaction. Currently, the identification of individuals with ASD largely relies on subjective assessments, presenting a challenge for researchers to distinguish between Typically Developing (TD) children and those with ASD. This study analyzes EEG data from 10 children with ASD and 10 TD children in response to an audio-video stimulus. Two separate analyses were performed on EEG frequency bands within the range of 0-70 Hz and specific frequency bands of 8-30 Hz, aiming to identify the brain lobe region that yields the most significant discrimination between ASD and TD. Parameters such as Linear Frequency Cepstral Coefficients (LFCC), Cepstral energy, signal energy, delta, and delta-delta derivatives were utilized for the analysis. The study deployed classification techniques including K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Decision Tree, Support Vector Machine (SVM), Bagging KNN, and Random Forest (RF). The results indicated that KNN surpassed all other classification models for frequency bands within the range of 0-70Hz, achieving a discriminating accuracy of 98.3% for ASD and TD in the central lobe region (C3, C4, Cz). However, KNN did not yield a significant level of accuracy when applied to a specific frequency band; it was improved by employing Bagging KNN, reaching 93.8% in the central lobe region (C3, C4, Cz). The electrode combination in the central lobe (C3, C4, and Cz) demonstrated superior discrimination between TD and ASD compared to other brain lobes.

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“…Such automated seizure detectors can trigger alarm when users are or will possibly be in a state of seizure. So far, algorithms for automated epileptic seizure detection proposed in most studies consist of three parts: (1) signal domain transformation, such as frequency domain via Fourier transform [3], wavelet time-frequency domain via discrete wavelet transform (DWT) [4,5], weighted and specific shapes via Hermite transformation [6] or original domain without transformation [7]; (2) feature extraction in the target domain, such as energy features [8] and complexity features [9]; and (3) machine learning based classification using a support vector machine (SVM) [10], k-nearest neighbor (KNN) [11] or artificial neural network (ANN) [12]. However, all the aforementioned three parts have shown limitations in some application scenarios, which are discussed in the following paragraphs separately.…”

Section: Introductionmentioning

confidence: 99%

Redundancy Removed Dual-Tree Discrete Wavelet Transform to Construct Compact Representations for Automated Seizure Detection

Jiang

Zhang

et al. 2019

Applied Sciences

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With the development of pervasive sensing and machine learning technologies, automated epileptic seizure detection based on electroencephalogram (EEG) signals has provided tremendous support for the lives of epileptic patients. Discrete wavelet transform (DWT) is an effective method for time-frequency analysis of EEG and has been used for seizure detection in daily healthcare monitoring systems. However, the shift variance, the lack of directionality and the substantial aliasing, limit the effects of DWT in some applications. Dual-tree discrete wavelet transform (DTDWT) can overcome those drawbacks but may increase information redundancy. For classification tasks with small dataset sizes, such redundancy can greatly reduce learning efficiency and model performance. In this work, we proposed a novel redundancy removed DTDWT (RR-DTDWT) framework for automated seizure detection. Energy and modified multi-scale entropy (MMSE) features in a dual tree wavelet domain were extracted to construct a complete picture of mental states. To the best of our knowledge, this is the first study to employ MMSE as an indicator of epileptic seizures. Moreover, a compact EEG representation can be obtained after removing useless information redundancy (redundancy between wavelet trees, adjacent channels and entropy scales) by a general auto-weighted feature selection framework via global redundancy minimization (AGRM). Through validation on Bonn and CHB-MIT databases, the proposed RR-DTDWT method can achieve better performance than previous studies.

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Analysis of MFCC Features for Eeg Signal Classification

Cited by 9 publications

References 11 publications

QU-GM: An IoT Based Glucose Monitoring System From Photoplethysmography, Blood Pressure, and Demographic Data Using Machine Learning

QU-GM: An IoT Based Glucose Monitoring System From Photoplethysmography, Blood Pressure, and Demographic Data Using Machine Learning

Autism Classification and Identification of Significant Brain Lobe Using Cepstral Coefficients

Redundancy Removed Dual-Tree Discrete Wavelet Transform to Construct Compact Representations for Automated Seizure Detection

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