scholarly journals Variable Self-Optimizing Cochlear Model for Heart Murmur Detection/Classification

2009 ◽  
Vol 3 (2) ◽  
Author(s):  
W. Ahmad ◽  
M. I. Hayee ◽  
G. Nordehn ◽  
S. Burns ◽  
J. L. Fitzakerley
Keyword(s):  
Neural Network ◽  
Coming Out ◽  
Human Heart ◽  
Electrical Signal ◽  
Heart Sounds ◽  
Final Decision ◽  
Heart Murmur ◽  
Filter Model ◽  
Human Cochlea

Accurate detection and classification of heart murmurs by auscultation is suboptimal and not always definitive. The murmur information perceived by the physician brain is the combined effect of both patient's (human) heart and the physician's ear. The information containing the murmur characterization which is retrieved by the human brain resides in the electrical signal coming out of the cochlea. For the very reasons described here, cochlea-like processing has been successfully applied to multiple speech recognition related technologies. This had not, before our prior work, been applied to human heart murmur analysis. Our prior research consisted of three steps: (1) capturing heart sounds, (2) processing the sounds using a cochlea-like filter, and then, (3) classifying each sound as being normal or a murmur using an artificial neural network (ANN). Previously in our research, we used a static cochlea-like filter model in step 2 as described above, which resulted a significant improvement in terms of accuracy of heart murmur classification. Our cochlear filter analysis helped identify information-rich frequency segments in human heart sound. We want to advance the cochlear filter model from a static to a variable frequency selective model with feedback from ANN for better optimization of the heart murmur classification. The heart sounds will be processed in ways more closely replicating the human cochlea than the static cochlear filter. A variable self optimizing cochlear filter will better reproduce the mechanism of the human cochlea in that it will contain a feedback system from ANN to cochlear processing to automatically select the most useful frequencies based upon a threshold mechanism filtering out those frequencies which do not contain significantly useful information about classification of heart murmur. The output of the sounds in the frequency range remaining (variable self-optimizing cochlear filtered sounds) may then be used by the neural network to make a final decision about murmur classification. Our hypothesis is that a variable self-optimizing cochlear filter will significantly improve the accuracy in classification of heart sounds as normal or murmur when compared to a static cochlear filter. Using this approach, we plan to develop an AI based system which will classify heart sounds with a success rate significantly better than the static cochlear filter previously developed.

scholarly journals Heart Murmur Detection∕Classification System Using Cochlea-Like Pre-Processing

2008 ◽  
Vol 2 (2) ◽  
Author(s):  
W. Ahmad ◽  
M. I. Hayee ◽  
Glenn Nordehn ◽  
S. Burns ◽  
Janet. L. Fitzakerley
Keyword(s):  
Coming Out ◽  
Heart Sound ◽  
Heart Association ◽  
Electrical Signal ◽  
Sound Signal ◽  
Heart Murmur ◽  
Permanent Disability ◽  
Novel Approach ◽  
Heart Sound Signal

According to the most recent report of American Heart Association (AHA), heart disease, stroke and other cardiovascular diseases continue to remain not only the no.1 killer of Americans but also a major cause of permanent disability among American workers. Recently, many research efforts have been carried out to apply artificial intelligence (AI) to auscultation based method for rigorous detection/classification of heart murmurs but accuracy rates are not always high. All of the proposed AI techniques rely on converting the heart sound to an electrical signal and processing that signal to optimize the AI for murmur detection and classification. However, all these techniques fail to recognize that the electrical signal coming out of the cochlea is very different than the electrical signal coming out of the microphone or any other electrical sensor which is commonly used for converting heart sound to electrical signal. In this research paper, we want to take a novel approach to pre-process the electrical heart sound signal before it goes to AI for murmur detection/classification by altering the electrical signal in a similar way as is done by the human cochlea before sending the signals to the brain. Our hypothesis is that cochlea like pre-processing will change the spectral contents of the heart sound signal to enhance the murmur information which can then be efficiently detected and classified by AI circuitry. Using this approach, we plan to develop an AI based system for heart murmur classification/ detection with success rate comparable to that of an expert cardiologist.

Decision support or autonomous artificial intelligence? The case of wrong blood in tube errors

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Christopher-John L. Farrell
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Computer Simulation ◽  
Clinical Chemistry ◽  
Human Interaction ◽  
Final Decision ◽  
P Values ◽  
Laboratory Staff

Abstract Objectives Artificial intelligence (AI) models are increasingly being developed for clinical chemistry applications, however, it is not understood whether human interaction with the models, which may occur once they are implemented, improves or worsens their performance. This study examined the effect of human supervision on an artificial neural network trained to identify wrong blood in tube (WBIT) errors. Methods De-identified patient data for current and previous (within seven days) electrolytes, urea and creatinine (EUC) results were used in the computer simulation of WBIT errors at a rate of 50%. Laboratory staff volunteers reviewed the AI model’s predictions, and the EUC results on which they were based, before making a final decision regarding the presence or absence of a WBIT error. The performance of this approach was compared to the performance of the AI model operating without human supervision. Results Laboratory staff supervised the classification of 510 sets of EUC results. This workflow identified WBIT errors with an accuracy of 81.2%, sensitivity of 73.7% and specificity of 88.6%. However, the AI model classifying these samples autonomously was superior on all metrics (p-values<0.05), including accuracy (92.5%), sensitivity (90.6%) and specificity (94.5%). Conclusions Human interaction with AI models can significantly alter their performance. For computationally complex tasks such as WBIT error identification, best performance may be achieved by autonomously functioning AI models.

Neural network classification of homomorphic segmented heart sounds

2007 ◽  
Vol 7 (1) ◽  
pp. 286-297 ◽  
Author(s):  
Cota Navin Gupta ◽  
Ramaswamy Palaniappan ◽  
Sundaram Swaminathan ◽  
Shankar M. Krishnan
Keyword(s):  
Neural Network ◽  
Heart Sounds ◽  
Neural Network Classification
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