Implementation of Fast-ICA: A Performance Based Comparison Between Floating Point and Fixed Point DSP Platform

Patil, Dinesh; Das, Niva; Routray, Aurobinda

doi:10.2478/v10048-011-0022-1

Cited by 12 publications

(6 citation statements)

References 6 publications

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“…The application of ICA is not limited to signal processing but also extends from separation of mixed voices and images, analysis of various types of data and data extraction in financial matters, data communication, system identification and several other biomedical signal processing tasks [24], [31]. The results presented in this paper appear to be promising.…”

Section: Application Of Ica On Eeg Signalmentioning

confidence: 82%

Artifact Removal from Biosignal using Fixed Point ICA Algorithm for Pre-processing in Biometric Recognition

Mishra¹,

Singla²

2013

Measurement Science Review

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In the modern world of automation, biological signals, especially Electroencephalogram (EEG) and Electrocardiogram (ECG), are gaining wide attention as a source of biometric information. Earlier studies have shown that EEG and ECG show versatility with individuals and every individual has distinct EEG and ECG spectrum. EEG (which can be recorded from the scalp due to the effect of millions of neurons) may contain noise signals such as eye blink, eye movement, muscular movement, line noise, etc. Similarly, ECG may contain artifact like line noise, tremor artifacts, baseline wandering, etc. These noise signals are required to be separated from the EEG and ECG signals to obtain the accurate results. This paper proposes a technique for the removal of eye blink artifact from EEG and ECG signal using fixed point or FastICA algorithm of Independent Component Analysis (ICA). For validation, FastICA algorithm has been applied to synthetic signal prepared by adding random noise to the Electrocardiogram (ECG) signal. FastICA algorithm separates the signal into two independent components, i.e. ECG pure and artifact signal. Similarly, the same algorithm has been applied to remove the artifacts (Electrooculogram or eye blink) from the EEG signal.

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Section: Application Of Ica On Eeg Signalmentioning

confidence: 82%

Artifact Removal from Biosignal using Fixed Point ICA Algorithm for Pre-processing in Biometric Recognition

Mishra¹,

Singla²

2013

Measurement Science Review

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“…The authors in Reference 15 presented a comparative study of a fixed‐point algorithm on a fixed‐point platform with respect to another floating‐point processor. ICA algorithm has been tested on a 32‐bit fixed point and a 32‐bit floating‐point digital signal processors (DSPs).…”

Section: Related Workmentioning

confidence: 99%

Improved ICA algorithm for ECG feature extraction and R‐peak detection

Jayasanthi

Ramamoorthy

Parthiban

2020

Adaptive Control & Signal

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SummaryElectrocardiogram (ECG) signal transmission and monitoring plays a paramount role in long‐term cardiac monitoring and analysis to provide remote health care in time, especially for the postoperative people and people in remote areas. The accuracy of ECG signals is of fundamental importance in cardiac diagnosis like R‐peak detection. So we need to incorporate analytical methods in existing healthcare systems, to capture more meaningful ECG components and to represent physical cardiac sources more clearly. With this aim, hardware optimized FCAICA (fast confluence adaptive independent component analysis) algorithm is proposed to extract pure ECG components from the ECG mixtures. The extracted signals are then subjected to R‐peak detection for further analysis. The proposed improved fast confluence adaptive independent component analysis (IFCAICA) method occupies less hardware resources, consumes low power, improves accuracy, and sensitivity in R‐peaks detection. In 0.18 nm technology, the IFCAICA consumes 10.13 mW of power and operates with 3.4 MHz operating frequency.

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“…Several different methods can be used for reduction of the acoustic noise generated in the MRI scanner [6]- [9]. The problem of processing the speech signal in the presence of noise may be solved by various techniques, e.g., the blind source separation by independent component analysis [10]. In our previous research, the noise reduction method was based on the fact that the mentioned acoustic noise of the MRI machine is a periodic signal with its fundamental frequency that may be filtered and processed in the spectral domain [11]- [12].…”

Section: Introductionmentioning

confidence: 99%

Two Methods of Automatic Evaluation of Speech Signal Enhancement Recorded in the Open-Air MRI Environment

Přibil

Přibilová²,

Frollo

2017

Measurement Science Review

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The paper focuses on two methods of evaluation of successfulness of speech signal enhancement recorded in the open-air magnetic resonance imager during phonation for the 3D human vocal tract modeling. The first approach enables to obtain a comparison based on statistical analysis by ANOVA and hypothesis tests. The second method is based on classification by Gaussian mixture models (GMM). The performed experiments have confirmed that the proposed ANOVA and GMM classifiers for automatic evaluation of the speech quality are functional and produce fully comparable results with the standard evaluation based on the listening test method.

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Implementation of Fast-ICA: A Performance Based Comparison Between Floating Point and Fixed Point DSP Platform

Abstract: The main focus of the paper is to bring out the differences in performance related issues of Fast-ICA algorithm associated with floating point and fixed point digital signal processing (

Cited by 12 publications

References 6 publications

Artifact Removal from Biosignal using Fixed Point ICA Algorithm for Pre-processing in Biometric Recognition

Artifact Removal from Biosignal using Fixed Point ICA Algorithm for Pre-processing in Biometric Recognition

Improved ICA algorithm for ECG feature extraction and R‐peak detection

Two Methods of Automatic Evaluation of Speech Signal Enhancement Recorded in the Open-Air MRI Environment

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