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Wyświetlanie 1-2 z 2
Tytuł:
Heart Rate Detection and Classification from Speech Spectral Features Using Machine Learning
Autorzy:
Usman, Mohammed
Zubair, Mohammed
Ahmad, Zeeshan
Zaidi, Monji
Ijyas, Thafasal
Parayangat, Muneer
Wajid, Mohd
Shiblee, Mohammad
Ali, Syed Jaffar
Powiązania:
https://bibliotekanauki.pl/articles/1953514.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
heart rate from speech
machine learning
MFCC
regression
classification
speech as a biomedical signal
Opis:
Measurement of vital signs of the human body such as heart rate, blood pressure, body temperature and respiratory rate is an important part of diagnosing medical conditions and these are usually measured using medical equipment. In this paper, we propose to estimate an important vital sign – heart rate from speech signals using machine learning algorithms. Existing literature, observation and experience suggest the existence of a correlation between speech characteristics and physiological, psychological as well as emotional conditions. In this work, we estimate the heart rate of individuals by applying machine learning based regression algorithms to Mel frequency cepstrum coefficients, which represent speech features in the spectral domain as well as the temporal variation of spectral features. The estimated heart rate is compared with actual measurement made using a conventional medical device at the time of recording speech. We obtain estimation accuracy close to 94% between the estimated and actual measured heart rate values. Binary classification of heart rate as ‘normal’ or ‘abnormal’ is also achieved with 100% accuracy. A comparison of machine learning algorithms in terms of heart rate estimation and classification accuracy is also presented. Heart rate measurement using speech has applications in remote monitoring of patients, professional athletes and can facilitate telemedicine.
Źródło:
Archives of Acoustics; 2021, 46, 1; 41-53
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Prioritizing and modelling of putative drug target proteins of Candida albicans by systems biology approach
Autorzy:
Ismail, Tariq
Fatima, Nighat
Muhammad, Syed
Zaidi, Syed
Rehman, Nisar
Hussain, Izhar
Tariq, Najam us
Amirzada, Imran
Mannan, Abdul
Powiązania:
https://bibliotekanauki.pl/articles/1038391.pdf
Data publikacji:
2018
Wydawca:
Polskie Towarzystwo Biochemiczne
Tematy:
Candida albicans
drug target identification
protein modeling
Opis:
Candida albicans (Candida albicans) is one of the major sources of nosocomial infections in humans which may prove fatal in 30% of cases. The hospital acquired infection is very difficult to treat affectively due to the presence of drug resistant pathogenic strains, therefore there is a need to find alternative drug targets to cure this infection. In silico and computational level frame work was used to prioritize and establish antifungal drug targets of Candida albicans. The identification of putative drug targets was based on acquiring 5090 completely annotated genes of Candida albicans from available databases which were categorized into essential and non-essential genes. The result indicated that 9% of proteins were essential and could become potential candidates for intervention which might result in pathogen eradication. We studied cluster of orthologs and the subtractive genomic analysis of these essential proteins against human genome was made as a reference to minimize the side effects. It was seen that 14% of Candida albicans proteins were evolutionary related to the human proteins while 86% are non-human homologs. In the next step of compatible drug target selections, the non-human homologs were sequentially compared to the human microbiome data to minimize the potential effects against gut flora which accumulated to 38% of the essential genome. The sub-cellular localization of these candidate proteins in fungal cellular systems indicated that 80% of them are cytoplasmic, 10% are mitochondrial and the remaining 10% are associated with the cell wall. The role of these non-human and non-gut flora putative target proteins in Candida albicans biological pathways was studied. Due to their integrated and critical role in Candida albicans replication cycle, four proteins were selected for molecular modeling. For drug designing and development, four high quality and reliable protein models with more than 70% sequence identity were constructed. These proteins are used for the docking studies of the known and new ligands (unpublished data). Our study will be an effective framework for drug target identifications of pathogenic microbial strains and development of new therapies against the infections they cause.
Źródło:
Acta Biochimica Polonica; 2018, 65, 2; 209-218
0001-527X
Pojawia się w:
Acta Biochimica Polonica
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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