1
|
Faisal MAA, Chowdhury MEH, Mahbub ZB, Pedersen S, Ahmed MU, Khandakar A, Alhatou M, Nabil M, Ara I, Bhuiyan EH, Mahmud S, AbdulMoniem M. NDDNet: a deep learning model for predicting neurodegenerative diseases from gait pattern. APPL INTELL 2023. [DOI: 10.1007/s10489-023-04557-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
|
2
|
Nazmul Islam Shuzan M, Chowdhury ME, Bin Ibne Reaz M, Khandakar A, Fuad Abir F, Ahasan Atick Faisal M, Hamid Md Ali S, Bakar AAA, Hossain Chowdhury M, Mahbub ZB, Monir Uddin M, Alhatou M. Machine learning-based classification of healthy and impaired gaits using 3D-GRF signals. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
|
3
|
Baray SB, Abdelmoniem M, Mahmud S, Kabir S, Faisal MAA, Chowdhury MEH, Abbas TO. Automated measurement of penile curvature using deep learning-based novel quantification method. Front Pediatr 2023; 11:1149318. [PMID: 37138577 PMCID: PMC10150132 DOI: 10.3389/fped.2023.1149318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Accepted: 03/13/2023] [Indexed: 05/05/2023] Open
Abstract
Objective Develop a reliable, automated deep learning-based method for accurate measurement of penile curvature (PC) using 2-dimensional images. Materials and methods A set of nine 3D-printed models was used to generate a batch of 913 images of penile curvature (PC) with varying configurations (curvature range 18° to 86°). The penile region was initially localized and cropped using a YOLOv5 model, after which the shaft area was extracted using a UNet-based segmentation model. The penile shaft was then divided into three distinct predefined regions: the distal zone, curvature zone, and proximal zone. To measure PC, we identified four distinct locations on the shaft that reflected the mid-axes of proximal and distal segments, then trained an HRNet model to predict these landmarks and calculate curvature angle in both the 3D-printed models and masked segmented images derived from these. Finally, the optimized HRNet model was applied to quantify PC in medical images of real human patients and the accuracy of this novel method was determined. Results We obtained a mean absolute error (MAE) of angle measurement <5° for both penile model images and their derivative masks. For real patient images, AI prediction varied between 1.7° (for cases of ∼30° PC) and approximately 6° (for cases of 70° PC) compared with assessment by a clinical expert. Discussion This study demonstrates a novel approach to the automated, accurate measurement of PC that could significantly improve patient assessment by surgeons and hypospadiology researchers. This method may overcome current limitations encountered when applying conventional methods of measuring arc-type PC.
Collapse
Affiliation(s)
- Sriman Bidhan Baray
- Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, Bangladesh
| | - Mohamed Abdelmoniem
- Department of Electrical Engineering, College of Engineering, Qatar University, Doha, Qatar
| | - Sakib Mahmud
- Department of Electrical Engineering, College of Engineering, Qatar University, Doha, Qatar
| | - Saidul Kabir
- Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, Bangladesh
| | | | | | - Tariq O. Abbas
- Department of Surgery, Weill Cornell Medicine-Qatar, Ar-Rayyan, Qatar
- Urology Division, Surgery Department, Sidra Medicine, Doha, Qatar
- College of Medicine, Qatar University, Doha, Qatar
- Correspondence: Tariq O. Abbas
| |
Collapse
|
4
|
Mahmud S, Ibtehaz N, Khandakar A, Sohel Rahman M, JR. Gonzales A, Rahman T, Shafayet Hossain M, Sakib Abrar Hossain M, Ahasan Atick Faisal M, Fuad Abir F, Musharavati F, E. H. Chowdhury M. NABNet: A Nested Attention-guided BiConvLSTM network for a robust prediction of Blood Pressure components from reconstructed Arterial Blood Pressure waveforms using PPG and ECG signals. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
5
|
Faisal MAA, Abir FF, Ahmed MU, Ahad MAR. Exploiting domain transformation and deep learning for hand gesture recognition using a low-cost dataglove. Sci Rep 2022; 12:21446. [PMID: 36509815 PMCID: PMC9743107 DOI: 10.1038/s41598-022-25108-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Accepted: 11/24/2022] [Indexed: 12/14/2022] Open
Abstract
Hand gesture recognition is one of the most widely explored areas under the human-computer interaction domain. Although various modalities of hand gesture recognition have been explored in the last three decades, in recent years, due to the availability of hardware and deep learning algorithms, hand gesture recognition research has attained renewed momentum. In this paper, we evaluate the effectiveness of a low-cost dataglove for classifying hand gestures in the light of deep learning. We have developed a cost-effective dataglove using five flex sensors, an inertial measurement unit, and a powerful microcontroller for onboard processing and wireless connectivity. We have collected data from 25 subjects for 24 static and 16 dynamic American sign language gestures for validating our system. Moreover, we proposed a novel Spatial Projection Image-based technique for dynamic hand gesture recognition. We also explored a parallel-path neural network architecture for handling multimodal data more effectively. Our method produced an F1-score of 82.19% for static gestures and 97.35% for dynamic gestures from a leave-one-out-cross-validation approach. Overall, this study demonstrates the promising performance of a generalized hand gesture recognition technique in hand gesture recognition. The dataset used in this work has been made publicly available.
Collapse
Affiliation(s)
- Md. Ahasan Atick Faisal
- grid.8198.80000 0001 1498 6059Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, 1000 Bangladesh
| | - Farhan Fuad Abir
- grid.8198.80000 0001 1498 6059Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, 1000 Bangladesh
| | - Mosabber Uddin Ahmed
- grid.8198.80000 0001 1498 6059Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, 1000 Bangladesh
| | - Md Atiqur Rahman Ahad
- grid.60969.300000 0001 2189 1306Department of Computer Science and Digital Technologies, University of East London, London, UK
| |
Collapse
|
6
|
Khandakar A, Mahmud S, Chowdhury MEH, Reaz MBI, Kiranyaz S, Mahbub ZB, Md Ali SH, Bakar AAA, Ayari MA, Alhatou M, Abdul-Moniem M, Faisal MAA. Design and Implementation of a Smart Insole System to Measure Plantar Pressure and Temperature. Sensors (Basel) 2022; 22:7599. [PMID: 36236697 PMCID: PMC9572216 DOI: 10.3390/s22197599] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/01/2022] [Accepted: 10/02/2022] [Indexed: 06/16/2023]
Abstract
An intelligent insole system may monitor the individual's foot pressure and temperature in real-time from the comfort of their home, which can help capture foot problems in their earliest stages. Constant monitoring for foot complications is essential to avoid potentially devastating outcomes from common diseases such as diabetes mellitus. Inspired by those goals, the authors of this work propose a full design for a wearable insole that can detect both plantar pressure and temperature using off-the-shelf sensors. The design provides details of specific temperature and pressure sensors, circuit configuration for characterizing the sensors, and design considerations for creating a small system with suitable electronics. The procedure also details how, using a low-power communication protocol, data about the individuals' foot pressure and temperatures may be sent wirelessly to a centralized device for storage. This research may aid in the creation of an affordable, practical, and portable foot monitoring system for patients. The solution can be used for continuous, at-home monitoring of foot problems through pressure patterns and temperature differences between the two feet. The generated maps can be used for early detection of diabetic foot complication with the help of artificial intelligence.
Collapse
Affiliation(s)
- Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
- Department of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Sakib Mahmud
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | | | - Mamun Bin Ibne Reaz
- Department of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Serkan Kiranyaz
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Zaid Bin Mahbub
- Department of Physics and Mathematics, North South University, Dhaka 1229, Bangladesh
| | - Sawal Hamid Md Ali
- Department of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Ahmad Ashrif A. Bakar
- Department of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Mohamed Arselene Ayari
- Department of Civil and Architectural Engineering, College of Engineering, Qatar University, Doha 2713, Qatar
- Technology Innovation and Engineering Education, College of Engineering, Qatar University, Doha 2713, Qatar
| | - Mohammed Alhatou
- Neuromuscular Division, Hamad General Hospital and Department of Neurology; Al Khor Hospital, Doha 3050, Qatar
| | | | | |
Collapse
|
7
|
Rahman T, Khandakar A, Abir FF, Faisal MAA, Hossain MS, Podder KK, Abbas TO, Alam MF, Kashem SB, Islam MT, Zughaier SM, Chowdhury MEH. QCovSML: A reliable COVID-19 detection system using CBC biomarkers by a stacking machine learning model. Comput Biol Med 2022; 143:105284. [PMID: 35180500 PMCID: PMC8839805 DOI: 10.1016/j.compbiomed.2022.105284] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 01/26/2022] [Accepted: 02/02/2022] [Indexed: 12/31/2022]
Abstract
The reverse transcription-polymerase chain reaction (RT-PCR) test is considered the current gold standard for the detection of coronavirus disease (COVID-19), although it suffers from some shortcomings, namely comparatively longer turnaround time, higher false-negative rates around 20-25%, and higher cost equipment. Therefore, finding an efficient, robust, accurate, and widely available, and accessible alternative to RT-PCR for COVID-19 diagnosis is a matter of utmost importance. This study proposes a complete blood count (CBC) biomarkers-based COVID-19 detection system using a stacking machine learning (SML) model, which could be a fast and less expensive alternative. This study used seven different publicly available datasets, where the largest one consisting of fifteen CBC biomarkers collected from 1624 patients (52% COVID-19 positive) admitted at San Raphael Hospital, Italy from February to May 2020 was used to train and validate the proposed model. White blood cell count, monocytes (%), lymphocyte (%), and age parameters collected from the patients during hospital admission were found to be important biomarkers for COVID-19 disease prediction using five different feature selection techniques. Our stacking model produced the best performance with weighted precision, sensitivity, specificity, overall accuracy, and F1-score of 91.44%, 91.44%, 91.44%, 91.45%, and 91.45%, respectively. The stacking machine learning model improved the performance in comparison to other state-of-the-art machine learning classifiers. Finally, a nomogram-based scoring system (QCovSML) was constructed using this stacking approach to predict the COVID-19 patients. The cut-off value of the QCovSML system for classifying COVID-19 and Non-COVID patients was 4.8. Six datasets from three different countries were used to externally validate the proposed model to evaluate its generalizability and robustness. The nomogram demonstrated good calibration and discrimination with the area under the curve (AUC) of 0.961 for the internal cohort and average AUC of 0.967 for all external validation cohort, respectively. The external validation shows an average weighted precision, sensitivity, F1-score, specificity, and overall accuracy of 92.02%, 95.59%, 93.73%, 90.54%, and 93.34%, respectively.
Collapse
Affiliation(s)
- Tawsifur Rahman
- Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar
| | - Farhan Fuad Abir
- Department of Electrical and Electronics Engineering, University of Dhaka, Dhaka, 1000, Bangladesh
| | - Md Ahasan Atick Faisal
- Department of Electrical and Electronics Engineering, University of Dhaka, Dhaka, 1000, Bangladesh
| | - Md Shafayet Hossain
- Dept. of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor, 43600, Malaysia
| | - Kanchon Kanti Podder
- Department of Biomedical Physics & Technology, University of Dhaka, Dhaka, 1000, Bangladesh
| | - Tariq O Abbas
- Urology Division, Surgery Department, Sidra Medicine, Doha, 26999, Qatar
| | - Mohammed Fasihul Alam
- Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, 2713, Qatar
| | - Saad Bin Kashem
- Department of Computing Science, AFG College with the University of Aberdeen, Doha, Qatar
| | - Mohammad Tariqul Islam
- Dept. of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor, 43600, Malaysia
| | - Susu M Zughaier
- Department of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, Doha, 2713, Qatar
| | | |
Collapse
|
8
|
Faisal MAA, Chowdhury MEH, Khandakar A, Hossain MS, Alhatou M, Mahmud S, Ara I, Sheikh SI, Ahmed MU. An investigation to study the effects of Tai Chi on human gait dynamics using classical machine learning. Comput Biol Med 2022; 142:105184. [PMID: 35016098 DOI: 10.1016/j.compbiomed.2021.105184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 12/16/2021] [Accepted: 12/26/2021] [Indexed: 11/03/2022]
Abstract
Tai Chi has been proven effective in preventing falls in older adults, improving the joint function of knee osteoarthritis patients, and improving the balance of stroke survivors. However, the effect of Tai Chi on human gait dynamics is still less understood. Studies conducted in this domain only relied on statistical and clinical measurements on the time-series gait data. In recent years machine learning has proven its ability in recognizing complex patterns from time-series data. In this research work, we have evaluated the performance of several machine learning algorithms in classifying the walking gait of Tai Chi masters (people expert on Tai Chi) from the normal subjects. The study is designed in a longitudinal manner where the Tai Chi naive subjects received 6 months of Tai Chi training and the data was recorded during the initial and follow-up sessions. A total of 57 subjects participated in the experiment among which 27 were Tai Chi masters. We have introduced a gender, BMI-based scaling of the features to mitigate their effects from the gait parameters. A hybrid feature ranking technique has also been proposed for selecting the best features for classification. The research reports 88.17% accuracy and 93.10% ROC AUC values from subject-wise 5-fold cross-validation for the Tai Chi masters' vs normal subjects' walking gait classification for the "Single-task" walking scenarios. We have also got fairly good accuracy for the "Dual-task" walking scenarios (82.62% accuracy and 84.11% ROC AUC values). The results indicate that Tai Chi clearly has an effect on the walking gait dynamics. The findings and methodology of this study could provide preliminary guidance for applying machine learning-based approaches to similar gait kinematics analyses.
Collapse
Affiliation(s)
- Md Ahasan Atick Faisal
- Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, 1000, Bangladesh
| | | | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar
| | - Md Shafayet Hossain
- Department of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor, 43600, Malaysia
| | - Mohammed Alhatou
- Neuromuscular Division, Hamad General Hospital and Department of Neurology, Alkhor Hospital, Doha, 3050, Qatar
| | - Sakib Mahmud
- Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar
| | - Iffat Ara
- Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar
| | - Shah Imran Sheikh
- Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar
| | - Mosabber Uddin Ahmed
- Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, 1000, Bangladesh.
| |
Collapse
|