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Article ; Online: Sensitivity of Epworth Sleepiness Scale in Detecting Obstructive Sleep Apnea in Pakistani Adults

Faisal Asad / Madiha Moin / Saif-ur-Rehman / Ubedullah

Liaquat National Journal of Primary Care, Vol 6, Iss 1, Pp 40-

2024 Volume 43

Abstract: Background: Self-reported measures of Excessive daytime Sleepiness, such as the Epworth Sleepiness Scale (ESS), have been widely used as a screening tool for OSA, but their accuracy in predicting OSA has been questioned. Objective: The main objective of ... ...

Abstract	Background: Self-reported measures of Excessive daytime Sleepiness, such as the Epworth Sleepiness Scale (ESS), have been widely used as a screening tool for OSA, but their accuracy in predicting OSA has been questioned. Objective: The main objective of this research is to re-evaluate the usage of ESS in predicting Obstructive Sleep Apnea (OSA) and to consider additional screening tools, such as polysomnography, to improve the accuracy of OSA diagnosis in the Pakistani population. Methods: It was a retrospective cross-sectional study design, conducted on 500 participants. Data was obtained from the hospital records at the Sleep Lab of Dow University Hospital from January 2021 to March 2023 who completed the Epworth Sleepiness Scale (ESS), received a clinical evaluation from a doctor, and underwent diagnostic polysomnography (PSG), polysomnography is a comprehensive sleep study that monitors various physiological parameters to diagnose sleep disorders. Results: Out of a total sample of 500 participants, 272 were males and 228 were females. The average age was 51 ± 12 years. The average body mass index (BMI) was 37.2 ± 8.1 Kg/m2 . The average Epworth Sleepiness Scale (ESS) score of the participants was 12.4 ± 4.2. The finding of our study shows Epworth sleepiness score is a good predictor of OSA (AUC: 0.84, 95% CI: 0.757-0.923). However, the optimal cutoff of ESS is 8.4 and above which shows sensitivity and specificity of 83.1% and 81.8% respectively. Conclusion: The results suggest that the Epworth Sleepiness Scale may be a useful tool for identifying individuals with OSA, although it also has a low false positive rate, there is a need for further research, and the importance of combining clinical assessment and diagnostic tests for accurate OSA diagnosis.
Keywords	excessive daytime sleepiness ; epworth score ; ess ; eds ; osa ; Medicine (General) ; R5-920
Subject code	150
Language	English
Publishing date	2024-04-01T00:00:00Z
Publisher	Liaquat National Hospital and Medical College
Document type	Article ; Online
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Article ; Online: Whole-genome SNP markers reveal runs of homozygosity in indigenous cattle breeds of Pakistan

Saif-ur-Rehman, Muhammad / Hassan, Faiz-ul / Reecy, James / Deng, Tingxian

Animal Biotechnology. 2023 Aug. 1, v. 34, no. 4 p.1384-1396

2023

Abstract: The runs of homozygosity (ROH) were identified in 14 Pakistani cattle breeds (n = 105) by genotyping with the Illumina 50 K SNP BeadChip. These breeds were categorized into Dairy, Dual, and Draft breeds based on their utility and production performance. ... ...

Abstract	The runs of homozygosity (ROH) were identified in 14 Pakistani cattle breeds (n = 105) by genotyping with the Illumina 50 K SNP BeadChip. These breeds were categorized into Dairy, Dual, and Draft breeds based on their utility and production performance. We identified a total of 10,936 ROHs which mainly consisted of a high number of shorter segments (1–4 Mb). Dairy group exhibited the highest level of inbreeding (FROH: 0.078 ± 0.028) while the lowest (FROH: 0.002 ± 0.008) was observed in Dual group. In 48 genomic regions identified with a high frequency of ROH, 207 genes were detected in the three breed groups. A substantially higher number of ROH islands detected in dairy breeds indicated the impact of the positive selection pressure over the years. Important candidate genes and QTL were detected in the ROH islands associated with economic traits like milk production, reproduction, meat, carcass, and health traits in dairy cattle.
Keywords	biotechnology ; dairy cattle ; genomics ; genotyping ; homozygosity ; meat ; milk production ; reproduction ; selection pressure ; Pakistan ; Runs of homozygosity ; indigenous cattle ; candidate genes ; QTL
Language	English
Dates of publication	2023-0801
Size	p. 1384-1396.
Publishing place	Taylor & Francis
Document type	Article ; Online
ZDB-ID	2043243-4
ISSN	1532-2378 ; 1049-5398
ISSN (online)	1532-2378
ISSN	1049-5398
DOI	10.1080/10495398.2022.2026369
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: A machine learning approach for Urdu text sentiment analysis

Muhammad Akhtar / Rana Saud Shoukat / Saif Ur Rehman

Mehran University Research Journal of Engineering and Technology, Vol 42, Iss 2, Pp 75-

2023 Volume 87

Abstract: Product evaluations, ratings, and other sorts of online expressions have risen in popularity as a result of the emergence of social networking sites and blogs. Sentiment analysis has emerged as a new area of study for computational linguists as a result ... ...

Abstract	Product evaluations, ratings, and other sorts of online expressions have risen in popularity as a result of the emergence of social networking sites and blogs. Sentiment analysis has emerged as a new area of study for computational linguists as a result of this rapidly expanding data set. From around a decade ago, this has been a topic of discussion for English speakers. However, the scientific community completely ignores other important languages, such as Urdu. Morphologically, Urdu is one of the most complex languages in the world. For this reason, a variety of unique characteristics, such as the language's unusual morphology and unrestricted word order, make the Urdu language processing a difficult challenge to solve. This research provides a new framework for the categorization of Urdu language sentiments. The main contributions of the research are to show how important this multidimensional research problem is as well as its technical parts, such as the parsing algorithm, corpus, lexicon, etc. A new approach for Urdu text sentiment analysis including data gathering, pre-processing, feature extraction, feature vector formation, and finally, sentiment classification has been designed to deal with Urdu language sentiments. The result and discussion section provides a comprehensive comparison of the proposed work with the standard baseline method in terms of precision, recall, f-measure, and accuracy of three different types of datasets. In the overall comparison of the models, the proposed work shows an encouraging achievement in terms of accuracy and other metrics. Last but not least, this section also provides the featured trend and possible direction of the current work.
Keywords	Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Science ; Q
Subject code	400
Language	English
Publishing date	2023-04-01T00:00:00Z
Publisher	Mehran University of Engineering and Technology
Document type	Article ; Online
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Article ; Online: ConTraNet: A hybrid network for improving the classification of EEG and EMG signals with limited training data.

Ali, Omair / Saif-Ur-Rehman, Muhammad / Glasmachers, Tobias / Iossifidis, Ioannis / Klaes, Christian

Computers in biology and medicine

2023 Volume 168, Page(s) 107649

Abstract: Objective: Bio-Signals such as electroencephalography (EEG) and electromyography (EMG) are widely used for the rehabilitation of physically disabled people and for the characterization of cognitive impairments. Successful decoding of these bio-signals ... ...

Abstract	Objective: Bio-Signals such as electroencephalography (EEG) and electromyography (EMG) are widely used for the rehabilitation of physically disabled people and for the characterization of cognitive impairments. Successful decoding of these bio-signals is however non-trivial because of the time-varying and non-stationary characteristics. Furthermore, existence of short- and long-range dependencies in these time-series signal makes the decoding even more challenging. State-of-the-art studies proposed Convolutional Neural Networks (CNNs) based architectures for the classification of these bio-signals, which are proven useful to learn spatial representations. However, CNNs because of the fixed size convolutional kernels and shared weights pay only uniform attention and are also suboptimal in learning short-long term dependencies, simultaneously, which could be pivotal in decoding EEG and EMG signals. Therefore, it is important to address these limitations of CNNs. To learn short- and long-range dependencies simultaneously and to pay more attention to more relevant part of the input signal, Transformer neural network-based architectures can play a significant role. Nonetheless, it requires a large corpus of training data. However, EEG and EMG decoding studies produce limited amount of the data. Therefore, using standalone transformers neural networks produce ordinary results. In this study, we ask a question whether we can fix the limitations of CNN and transformer neural networks and provide a robust and generalized model that can simultaneously learn spatial patterns, long-short term dependencies, pay variable amount of attention to time-varying non-stationary input signal with limited training data. Approach: In this work, we introduce a novel single hybrid model called ConTraNet, which is based on CNN and Transformer architectures that contains the strengths of both CNN and Transformer neural networks. ConTraNet uses a CNN block to introduce inductive bias in the model and learn local dependencies, whereas the Transformer block uses the self-attention mechanism to learn the short- and long-range or global dependencies in the signal and learn to pay different attention to different parts of the signals. Main results: We evaluated and compared the ConTraNet with state-of-the-art methods on four publicly available datasets (BCI Competition IV dataset 2b, Physionet MI-EEG dataset, Mendeley sEMG dataset, Mendeley sEMG V1 dataset) which belong to EEG-HMI and EMG-HMI paradigms. ConTraNet outperformed its counterparts in all the different category tasks (2-class, 3-class, 4-class, 7-class, and 10-class decoding tasks). Significance: With limited training data ConTraNet significantly improves classification performance on four publicly available datasets for 2, 3, 4, 7, and 10-classes compared to its counterparts.
MeSH term(s)	Humans ; Machine Learning ; Brain-Computer Interfaces ; Movement ; Neural Networks, Computer ; Algorithms ; Electroencephalography/methods ; Imagination
Language	English
Publishing date	2023-11-02
Publishing country	United States
Document type	Journal Article
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2023.107649
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Book ; Online: Adaptive SpikeDeep-Classifier

Saif-ur-Rehman, Muhammad / Ali, Omair / Klaes, Christian / Iossifidis, Ioannis

Self-organizing and self-supervised machine learning algorithm for online spike sorting

2023

Abstract: Objective. Research on brain-computer interfaces (BCIs) is advancing towards rehabilitating severely disabled patients in the real world. Two key factors for successful decoding of user intentions are the size of implanted microelectrode arrays and a ... ...

Abstract	Objective. Research on brain-computer interfaces (BCIs) is advancing towards rehabilitating severely disabled patients in the real world. Two key factors for successful decoding of user intentions are the size of implanted microelectrode arrays and a good online spike sorting algorithm. A small but dense microelectrode array with 3072 channels was recently developed for decoding user intentions. The process of spike sorting determines the spike activity (SA) of different sources (neurons) from recorded neural data. Unfortunately, current spike sorting algorithms are unable to handle the massively increasing amount of data from dense microelectrode arrays, making spike sorting a fragile component of the online BCI decoding framework. Approach. We proposed an adaptive and self-organized algorithm for online spike sorting, named Adaptive SpikeDeep-Classifier (Ada-SpikeDeepClassifier), which uses SpikeDeeptector for channel selection, an adaptive background activity rejector (Ada-BAR) for discarding background events, and an adaptive spike classifier (Ada-Spike classifier) for classifying the SA of different neural units. Results. Our algorithm outperformed our previously published SpikeDeep-Classifier and eight other spike sorting algorithms, as evaluated on a human dataset and a publicly available simulated dataset. Significance. The proposed algorithm is the first spike sorting algorithm that automatically learns the abrupt changes in the distribution of noise and SA. It is an artificial neural network-based algorithm that is well-suited for hardware implementation on neuromorphic chips that can be used for wearable invasive BCIs.
Keywords	Quantitative Biology - Neurons and Cognition ; Computer Science - Artificial Intelligence ; Computer Science - Information Theory ; Computer Science - Machine Learning ; I.2.8 ; I.2.6
Subject code	006
Publishing date	2023-03-30
Publishing country	us
Document type	Book ; Online
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Article ; Online: Covid 19 image classification using hybrid averaging transfer learning model

Qamar Abbas / Khalid Mahmood / Saif Ur Rehman / Muhammad Imran

Mehran University Research Journal of Engineering and Technology, Vol 42, Iss 4, Pp 72-

2023 Volume 83

Abstract: The outbreak of Corona Virus 2019(Covid-19) is a great threat to the whole world. It is crucial to early detect patients infected with covid-19 and treat them to mitigate the rapid spread of this disease. It is an immediate priority to overcome the ... ...

Abstract	The outbreak of Corona Virus 2019(Covid-19) is a great threat to the whole world. It is crucial to early detect patients infected with covid-19 and treat them to mitigate the rapid spread of this disease. It is an immediate priority to overcome the traditional screening and develop an accurate as well as speedy covid-19 automatic diagnosis system. Computer Tomography (CT) and Chest X-Ray imaging coupled with deep learning models to develop and test Computer Aided Screening (CAS) of covid-19 images from the normal images. In this paper classification and screening of covid-19 disease are performed by using pre-trained convolutional neural networks and a proposed hybrid model on an available standard dataset of chest X-Ray images. The proposed hybrid model employs the pre-trained Convolutional Neural Network models and Transfer Learning models. Our proposed model consists of three stages where extraction of features is performed in first stage by using pre-trained machine learning model. Deep features are extracted by using the infusion of the Transfer Learning Technique in the second stage of the model. The third stage uses Flatten and Classification layers to diagnose of Covid-19 patients. In order to assure the consistency of the proposed model, by considering standard dataset X-Ray images. Simulation results of performance metrics of Accuracy, F1 Score, Precision, Recall, ROC, and AUC curve, and training and testing loss are used to evaluate and compare the proposed model with existing models. Experimental result demonstrates that the hybrid model improves the screening process for Covid-19 disease by achieving higher accuracy.
Keywords	Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Science ; Q
Subject code	006
Language	English
Publishing date	2023-09-01T00:00:00Z
Publisher	Mehran University of Engineering and Technology
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Enhancing the decoding accuracy of EEG signals by the introduction of anchored-STFT and adversarial data augmentation method.

Ali, Omair / Saif-Ur-Rehman, Muhammad / Dyck, Susanne / Glasmachers, Tobias / Iossifidis, Ioannis / Klaes, Christian

Scientific reports

2022 Volume 12, Issue 1, Page(s) 4245

Abstract: Brain-computer interfaces (BCIs) enable communication between humans and machines by translating brain activity into control commands. Electroencephalography (EEG) signals are one of the most used brain signals in non-invasive BCI applications but are ... ...

Abstract	Brain-computer interfaces (BCIs) enable communication between humans and machines by translating brain activity into control commands. Electroencephalography (EEG) signals are one of the most used brain signals in non-invasive BCI applications but are often contaminated with noise. Therefore, it is possible that meaningful patterns for classifying EEG signals are deeply hidden. State-of-the-art deep-learning algorithms are successful in learning hidden, meaningful patterns. However, the quality and the quantity of the presented inputs are pivotal. Here, we propose a feature extraction method called anchored Short Time Fourier Transform (anchored-STFT), which is an advanced version of STFT, as it minimizes the trade-off between temporal and spectral resolution presented by STFT. In addition, we propose a data augmentation method derived from l2-norm fast gradient sign method (FGSM), called gradient norm adversarial augmentation (GNAA). GNAA is not only an augmentation method but is also used to harness adversarial inputs in EEG data, which not only improves the classification accuracy but also enhances the robustness of the classifier. In addition, we also propose a CNN architecture, namely Skip-Net, for the classification of EEG signals. The proposed pipeline outperforms the current state-of-the-art methods and yields classification accuracies of 90.7% on BCI competition II dataset III and 89.5%, 81.8%, 76.0% and 85.4%, 69.1%, 80.9% on different data distributions of BCI Competition IV dataset 2b and 2a, respectively.
MeSH term(s)	Algorithms ; Brain-Computer Interfaces ; Electroencephalography/methods ; Fourier Analysis ; Humans ; Imagination ; Neural Networks, Computer
Language	English
Publishing date	2022-03-10
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-022-07992-w
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Article ; Online: Whole-genome SNP markers reveal runs of homozygosity in indigenous cattle breeds of Pakistan.

Saif-Ur-Rehman, Muhammad / Hassan, Faiz-Ul / Reecy, James / Deng, Tingxian

Animal biotechnology

2022 Volume 34, Issue 4, Page(s) 1384–1396

Abstract: The runs of homozygosity (ROH) were identified in 14 Pakistani cattle breeds ( ...

Abstract	The runs of homozygosity (ROH) were identified in 14 Pakistani cattle breeds (
MeSH term(s)	Cattle/genetics ; Animals ; Pakistan ; Polymorphism, Single Nucleotide/genetics ; Homozygote ; Inbreeding ; Genome/genetics ; Genotype
Language	English
Publishing date	2022-01-19
Publishing country	England
Document type	Journal Article
ZDB-ID	2043243-4
ISSN	1532-2378 ; 1049-5398
ISSN (online)	1532-2378
ISSN	1049-5398
DOI	10.1080/10495398.2022.2026369
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Article ; Online: Genetic variants of

Asim, Muhammad / Saif-Ur Rehman, Muhammad / Hassan, Faiz-Ul / Awan, Faisal Saeed

Animal biotechnology

2022 Volume 34, Issue 7, Page(s) 2951–2962

Abstract: Milk protein genes are associated with milk yield and composition in dairy animals. The present study aimed to identify milk protein genes ( ...

Abstract	Milk protein genes are associated with milk yield and composition in dairy animals. The present study aimed to identify milk protein genes (
MeSH term(s)	Female ; Cattle/genetics ; Animals ; Buffaloes/genetics ; Caseins/genetics ; Caseins/metabolism ; Milk/chemistry ; Milk Proteins/genetics ; Polymorphism, Single Nucleotide/genetics ; Lactation/genetics
Chemical Substances	Caseins ; Milk Proteins
Language	English
Publishing date	2022-09-27
Publishing country	England
Document type	Journal Article
ZDB-ID	2043243-4
ISSN	1532-2378 ; 1049-5398
ISSN (online)	1532-2378
ISSN	1049-5398
DOI	10.1080/10495398.2022.2126365
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Article ; Online: Deep transfer learning compared to subject-specific models for sEMG decoders.

Lehmler, Stephan Johann / Saif-Ur-Rehman, Muhammad / Tobias, Glasmachers / Iossifidis, Ioannis

Journal of neural engineering

2022 Volume 19, Issue 5

Abstract: ... ...

Abstract	Objective
MeSH term(s)	Humans ; Electromyography/methods ; Algorithms ; Artificial Limbs ; Calibration ; Machine Learning
Language	English
Publishing date	2022-10-20
Publishing country	England
Document type	Journal Article
ZDB-ID	2170901-4
ISSN	1741-2552 ; 1741-2560
ISSN (online)	1741-2552
ISSN	1741-2560
DOI	10.1088/1741-2552/ac9860
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