Výsledky vyhledávání - "Jarchi, Delaram"

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A Deep Learning Architecture with Spatio-Temporal Focusing for Detecting Respiratory Anomalies

Autor: Ngo, Dat, Pham, Lam, Phan, Huy, Tran, Minh, Jarchi, Delaram

This paper presents a deep learning system applied for detecting anomalies from respiratory sound recordings. Our system initially performs audio feature extraction using Continuous Wavelet transformation. This transformation converts the respiratory

Externí odkaz: http://arxiv.org/abs/2306.14929

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An Inception-Residual-Based Architecture with Multi-Objective Loss for Detecting Respiratory Anomalies

Autor: Ngo, Dat, Pham, Lam, Phan, Huy, Tran, Minh, Jarchi, Delaram, Kolozali, Sefki

This paper presents a deep learning system applied for detecting anomalies from respiratory sound recordings. Initially, our system begins with audio feature extraction using Gammatone and Continuous Wavelet transformation. This step aims to transfor

Externí odkaz: http://arxiv.org/abs/2303.04104

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Audio-Based Deep Learning Frameworks for Detecting COVID-19

Autor: Ngo, Dat, Pham, Lam, Hoang, Truong, Kolozali, Sefki, Jarchi, Delaram

This paper evaluates a wide range of audio-based deep learning frameworks applied to the breathing, cough, and speech sounds for detecting COVID-19. In general, the audio recording inputs are transformed into low-level spectrogram features, then they

Externí odkaz: http://arxiv.org/abs/2202.05626

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Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning

Autor: Jarchi, Delaram, Andreu-Perez, Javier, Kiani, Mehrin, Vysata, Oldrich, Kuchynka, Jiri, Prochazka, Ales, Sane, Saeid

Publikováno v: Sensors 20.9 (2020): 2594

Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) hav

Externí odkaz: http://arxiv.org/abs/2111.05917

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A Generic Deep Learning Based Cough Analysis System from Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levels

Autor: Andreu-Perez, Javier, Pérez-Espinosa, Humberto, Timonet, Eva, Kiani, Mehrin, Girón-Pérez, Manuel I., Benitez-Trinidad, Alma B., Jarchi, Delaram, Rosales-Pérez, Alejandro, Gatzoulis, Nick, Reyes-Galaviz, Orion F., Torres-García, Alejandro, Reyes-García, Carlos A., Ali, Zulfiqar, Rivas, Francisco

Publikováno v: IEEE Transactions on Services Computing (2021)

We seek to evaluate the detection performance of a rapid primary screening tool of Covid-19 solely based on the cough sound from 8,380 clinically validated samples with laboratory molecular-test (2,339 Covid-19 positives and 6,041 Covid-19 negatives)

Externí odkaz: http://arxiv.org/abs/2111.05895

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