Information Fusion for Perceptual Feedback: A Brain Activity Sonification Approach

Rutkowski, Tomasz M.; Cichocki, Andrzej; Mandic, Danilo P.

doi:10.1007/978-0-387-74367-7_14

“…A direct mapping to MIDI of the TF map of the wavelet will result in a very noisy signal, due to the constant background respiration sounds. Rutkowski et al [27] used both z-score and bump source modeling to achieve a better suited TF map to be used in the mapping onto MIDI. The main idea is to set predefined elementary parameterized functions called bumps whereby the TF map is represented by a set of these bumps.…”

Section: Wavelet and Bump Source (Wbs) Sonificationmentioning

confidence: 99%

Audio display and environmental sound analysis of diagnostic and therapeutic respiratory sounds

Garingo¹

2023

Preprint

0

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The objective of this study is to provide a framework to aid physicians in identifying early respiratory ailments as well as provide a means of monitoring medication compliancy for both the patient and physicians. To aid physicians identify abnormal sounds during auscultations such as crackle, this work proposes a multimedia approach in the form of audio display (AD) to enhance crackle sounds produced in respiration. This work utilize a two step AD approach in which the crackle sound is first separated from the rest of the vesicular sound and then either sonified or audified. To aid in monitoring use of medication this work proposes an environmental sound analysis (ESA) framework to autonomously quantify adherence to medication. This work employed traditional audio features to extract meaningful discriminatory information to identify the inhaler sounds from the environment with the aid of maximum relevance and minimum redundancy algorithm and the hidden markov model.

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“…A direct mapping to MIDI of the TF map of the wavelet will result in a very noisy signal, due to the constant background respiration sounds. Rutkowski et al [27] used both z-score and bump source modeling to achieve a better suited TF map to be used in the mapping onto MIDI. The main idea is to set predefined elementary parameterized functions called bumps whereby the TF map is represented by a set of these bumps.…”

Section: Wavelet and Bump Source (Wbs) Sonificationmentioning

confidence: 99%

Audio display and environmental sound analysis of diagnostic and therapeutic respiratory sounds

Garingo¹

2023

Preprint

0

View full text Add to dashboard Cite

The objective of this study is to provide a framework to aid physicians in identifying early respiratory ailments as well as provide a means of monitoring medication compliancy for both the patient and physicians. To aid physicians identify abnormal sounds during auscultations such as crackle, this work proposes a multimedia approach in the form of audio display (AD) to enhance crackle sounds produced in respiration. This work utilize a two step AD approach in which the crackle sound is first separated from the rest of the vesicular sound and then either sonified or audified. To aid in monitoring use of medication this work proposes an environmental sound analysis (ESA) framework to autonomously quantify adherence to medication. This work employed traditional audio features to extract meaningful discriminatory information to identify the inhaler sounds from the environment with the aid of maximum relevance and minimum redundancy algorithm and the hidden markov model.

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“…From the decomposed signals through observation, how can we ensure that these IMFs whether had established the completeness of the decomposition or the dif-ference between the reconstructed data from the sum of all IMFs and the original data were exists? One of the methods to solve this question is to check the orthogonality of IMF components which had been proposed by [14]. The index of orthogonality can be calculated and the interpolation techniques can be evaluated from the According to the [6], the orthogonality is required in linear decomposition systems; it might not make physical sense for nonlinear decomposition as in EMD and EEMD.…”

Section: Open Accessmentioning

confidence: 99%

Sleep disorder detection and identification

Tan¹,

Leong²

2012

JBiSE

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Electroencephalogram (EEG) is one of the medical devices that used for sleep disorder detection. Sleep disorder such as Obstructive Sleep Apnea Syndrome (OSAS) often appears during sleep event. Since the OSAS patients have the difficulties to allow the airflow into the lung while inspiration, the EEG is applied to capture and record the brainwave of the patient. In this work, the Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) are used to process and analyze the accuracy and efficiency of the results. Both of these methods will decompose the EEG signal into a collection of Intrinsic Mode Function (IMF). In this paper, index orthogonality has been calculated to indicate the completeness of the decomposed signal with the original signal. The instantaneous frequency and Hilbert Spectrum based on both methods also employed by IMF to analyze and present the results in frequency-time distribution to determine the characteristic of the inherent properties of signal. Besides, Hilbert marginal spectrum has been applied to measure the total amplitude contribution from each frequency value. Finally, the results shown that the EEMD is better in solving mode mixing problem and better improvement over EMD method

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Information Fusion for Perceptual Feedback: A Brain Activity Sonification Approach

Cited by 11 publications

References 6 publications

Audio display and environmental sound analysis of diagnostic and therapeutic respiratory sounds

Audio display and environmental sound analysis of diagnostic and therapeutic respiratory sounds

Audio display and environmental sound analysis of diagnostic and therapeutic respiratory sounds

Sleep disorder detection and identification

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