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Article ; Online: Fusion of pattern-based and statistical features for Schizophrenia detection from EEG signals.

Agarwal, Megha / Singhal, Amit

2023 Volume 112, Page(s) 103949

Abstract: Schizophrenia (SZ) is a chronic disorder affecting the functioning of the brain. It can lead to irrational behaviour amongst the patients suffering from this disease. A low-cost diagnostic needs to be developed for SZ so that timely treatment can be ... ...

Abstract	Schizophrenia (SZ) is a chronic disorder affecting the functioning of the brain. It can lead to irrational behaviour amongst the patients suffering from this disease. A low-cost diagnostic needs to be developed for SZ so that timely treatment can be provided to the patients. In this work, we propose an accurate and easy-to-implement system to detect SZ using electroencephalogram (EEG) signals. The signal is divided into sub-band components by a Fourier-based technique that can be implemented in real-time using fast Fourier transform. Thereafter, statistical features are computed from these components. Further, look ahead pattern (LAP) is developed as a feature to capture local variations in the EEG signal. The fusion of these two distinct schemes enables a thorough examination of EEG signals. Kruskal-Wallis test is utilized for the selection of significant features. Various machine learning classifiers are employed and the proposed framework achieves 98.62% and 99.24% accuracy in identifying SZ cases, considering two distinct datasets, using boosted trees classifier. This method provides a promising candidate for widespread deployment in efficient real-time systems for SZ detection.
MeSH term(s)	Humans ; Schizophrenia/diagnosis ; Support Vector Machine ; Electroencephalography/methods ; Brain ; Algorithms
Language	English
Publishing date	2023-01-04
Publishing country	England
Document type	Journal Article
ZDB-ID	1181080-4
ISSN	1873-4030 ; 1350-4533
ISSN (online)	1873-4030
ISSN	1350-4533
DOI	10.1016/j.medengphy.2023.103949
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Article ; Online: ECG arrhythmia detection in an inter-patient setting using Fourier decomposition and machine learning.

Fatimah, Binish / Singhal, Amit / Singh, Pushpendra

Medical engineering & physics

2024 Volume 124, Page(s) 104102

Abstract: ECG beat classification or arrhythmia detection through artificial intelligence (AI) is an active topic of research. It is vital to recognize and detect the type of arrhythmia for monitoring cardiac abnormalities. The AI-based ECG beat classification ... ...

Abstract	ECG beat classification or arrhythmia detection through artificial intelligence (AI) is an active topic of research. It is vital to recognize and detect the type of arrhythmia for monitoring cardiac abnormalities. The AI-based ECG beat classification algorithms proposed in the literature suffer from two main drawbacks. Firstly, some of the works have not considered any unseen test data to validate the performance of their algorithms. Secondly, the accuracy of detecting superventricular ectopic beats (SVEB) needs to be improved. In this work, we address these issues by considering an inter-patient paradigm where the test dataset is collected from a different set of subjects than the training data. Also, the proposed methodology detects SVEB with an F1 score of 89.35%, which is better than existing algorithms. We have used the Fourier decomposition method (FDM) for multi-scale analysis of ECG signals and extracted time-domain and statistical features from the narrow-band signal components obtained using FDM. Feature selection techniques, including the Kruskal-Wallis test and minimum redundancy maximum relevance (mRMR) have been used to select only the relevant features and rank these features to remove any redundancy. Since the dataset used is highly imbalanced, Mathew's correlation coefficient (MCC) has also been used to analyze the performance of the proposed method. Support vector machine classifier with linear kernel achieves an overall 98.03% accuracy and 91.84% MCC for the MIT-BIH arrhythmia dataset.
MeSH term(s)	Humans ; Artificial Intelligence ; Signal Processing, Computer-Assisted ; Electrocardiography ; Algorithms ; Arrhythmias, Cardiac/diagnosis ; Support Vector Machine ; Heart Rate
Language	English
Publishing date	2024-01-09
Publishing country	England
Document type	Journal Article
ZDB-ID	1181080-4
ISSN	1873-4030 ; 1350-4533
ISSN (online)	1873-4030
ISSN	1350-4533
DOI	10.1016/j.medengphy.2024.104102
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Article ; Online: A two-stage transformer based network for motor imagery classification.

Chaudhary, Priyanshu / Dhankhar, Nischay / Singhal, Amit / Rana, K P S

Medical engineering & physics

2024 , Page(s) 104154

Abstract: Brain-computer interfaces (BCIs) are used to understand brain functioning and develop therapies for neurological and neurodegenerative disorders. Therefore, BCIs are crucial in rehabilitating motor dysfunction and advancing motor imagery applications. ... ...

Abstract	Brain-computer interfaces (BCIs) are used to understand brain functioning and develop therapies for neurological and neurodegenerative disorders. Therefore, BCIs are crucial in rehabilitating motor dysfunction and advancing motor imagery applications. For motor imagery, electroencephalogram (EEG) signals are used to classify the subject's intention of moving a body part without actually moving it. This paper presents a two-stage transformer-based architecture that employs handcrafted features and deep learning techniques to enhance the classification performance on benchmarked EEG signals. Stage-1 is built on parallel convolution based EEGNet, multi-head attention, and separable temporal convolution networks for spatiotemporal feature extraction. Further, for enhanced classification, in stage-2, additional features and embeddings extracted from stage-1 are used to train TabNet. In addition, a novel channel cluster swapping data augmentation technique is also developed to handle the issue of limited samples for training deep learning architectures. The developed two-stage architecture offered an average classification accuracy of 88.5 % and 88.3 % on the BCI Competition IV-2a and IV-2b datasets, respectively, which is approximately 3.0 % superior over similar recent reported works.
Language	English
Publishing date	2024-03-20
Publishing country	England
Document type	Journal Article
ZDB-ID	1181080-4
ISSN	1873-4030 ; 1350-4533
ISSN (online)	1873-4030
ISSN	1350-4533
DOI	10.1016/j.medengphy.2024.104154
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Article ; Online: Case 1: A Preterm Neonate with Polyhydramnios, Polyuria, and Hearing Loss.

Singhal, Amit / Vishnu Tewari, Vishal

NeoReviews

2021 Volume 22, Issue 3, Page(s) e189–e193

MeSH term(s)	Female ; Hearing Loss/diagnosis ; Humans ; Infant, Newborn ; Polyhydramnios/diagnostic imaging ; Polyuria/diagnosis ; Pregnancy
Language	English
Publishing date	2021-02-06
Publishing country	United States
Document type	Case Reports ; Journal Article
ISSN	1526-9906
ISSN (online)	1526-9906
DOI	10.1542/neo.22-3-e189
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Article ; Online: Deep temporal networks for EEG-based motor imagery recognition.

Sharma, Neha / Upadhyay, Avinash / Sharma, Manoj / Singhal, Amit

Scientific reports

2023 Volume 13, Issue 1, Page(s) 18813

Abstract: The electroencephalogram (EEG) based motor imagery (MI) signal classification, also known as motion recognition, is a highly popular area of research due to its applications in robotics, gaming, and medical fields. However, the problem is ill-posed as ... ...

Abstract	The electroencephalogram (EEG) based motor imagery (MI) signal classification, also known as motion recognition, is a highly popular area of research due to its applications in robotics, gaming, and medical fields. However, the problem is ill-posed as these signals are non-stationary and noisy. Recently, a lot of efforts have been made to improve MI signal classification using a combination of signal decomposition and machine learning techniques but they fail to perform adequately on large multi-class datasets. Previously, researchers have implemented long short-term memory (LSTM), which is capable of learning the time-series information, on the MI-EEG dataset for motion recognition. However, it can not model very long-term dependencies present in the motion recognition data. With the advent of transformer networks in natural language processing (NLP), the long-term dependency issue has been widely addressed. Motivated by the success of transformer algorithms, in this article, we propose a transformer-based deep learning neural network architecture that performs motion recognition on the raw BCI competition III IVa and IV 2a datasets. The validation results show that the proposed method achieves superior performance than the existing state-of-the-art methods. The proposed method produces classification accuracy of 99.7% and 84% on the binary class and the multi-class datasets, respectively. Further, the performance of the proposed transformer-based model is also compared with LSTM.
MeSH term(s)	Movement ; Brain-Computer Interfaces ; Neural Networks, Computer ; Imagery, Psychotherapy/methods ; Algorithms ; Electroencephalography/methods ; Imagination
Language	English
Publishing date	2023-11-01
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-41653-w
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Article ; Online: Deep temporal networks for EEG-based motor imagery recognition

Neha Sharma / Avinash Upadhyay / Manoj Sharma / Amit Singhal

Scientific Reports, Vol 13, Iss 1, Pp 1-

2023 Volume 12

Abstract: Abstract The electroencephalogram (EEG) based motor imagery (MI) signal classification, also known as motion recognition, is a highly popular area of research due to its applications in robotics, gaming, and medical fields. However, the problem is ill- ... ...

Abstract	Abstract The electroencephalogram (EEG) based motor imagery (MI) signal classification, also known as motion recognition, is a highly popular area of research due to its applications in robotics, gaming, and medical fields. However, the problem is ill-posed as these signals are non-stationary and noisy. Recently, a lot of efforts have been made to improve MI signal classification using a combination of signal decomposition and machine learning techniques but they fail to perform adequately on large multi-class datasets. Previously, researchers have implemented long short-term memory (LSTM), which is capable of learning the time-series information, on the MI-EEG dataset for motion recognition. However, it can not model very long-term dependencies present in the motion recognition data. With the advent of transformer networks in natural language processing (NLP), the long-term dependency issue has been widely addressed. Motivated by the success of transformer algorithms, in this article, we propose a transformer-based deep learning neural network architecture that performs motion recognition on the raw BCI competition III IVa and IV 2a datasets. The validation results show that the proposed method achieves superior performance than the existing state-of-the-art methods. The proposed method produces classification accuracy of 99.7% and 84% on the binary class and the multi-class datasets, respectively. Further, the performance of the proposed transformer-based model is also compared with LSTM.
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2023-11-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Do age, gender, BMI and disease duration influence the clinical outcomes in patients of knee osteoarthritis treated with serial injections of autologous platelet rich plasma?

Saraf, Amit / Hussain, Altaf / Singhal, Ayush / Arora, Vaneet / Bishnoi, Sandeep

Journal of clinical orthopaedics and trauma

2023 Volume 43, Page(s) 102226

Abstract: Purpose: To study whether age, gender, body mass index(BMI) and disease duration influence the clinical outcomes in kellgren-Lawrence(K-L) grade II,III knee osteoarthritis(KOA) patients treated with serial injections of platelet rich plasma(PRP).: ... ...

Abstract	Purpose: To study whether age, gender, body mass index(BMI) and disease duration influence the clinical outcomes in kellgren-Lawrence(K-L) grade II,III knee osteoarthritis(KOA) patients treated with serial injections of platelet rich plasma(PRP). Patients and methods: 65 patients were given three monthly intra-articular injections of PRP in this prospective interventional study. The patients were divided into subgroups depending on the factor studied: by age(in years) into young <45(n = 7), middle age 45-60(n = 35), and elderly >60(n = 23): by BMI(in kg/m Results: Mean VAS and WOMAC scores showed a statistically significant improvement (P < 0.0001) across all groups and subgroups (age,gender,BMI,disease duration) at follow up. On intra-subgroup comparison, we found no significant differences(P > 0.05) among age, BMI or gender subgroups, however the scores were significantly better in patients with disease duration of less than 1 year than those with more than 1 year duration at both 6 and 9 months[P < 0.001(RC = 9.630,95% CI = 4.037-15.222,P = 0.001)]. Conclusion: PRP injections if given serially can improve the short term subjective scores of VAS and WOMAC scores in patients with K-L grade II and III KOA irrespective of age, gender, BMI or disease duration, however, clinical benefits can be maximized if given early in the disease course.
Language	English
Publishing date	2023-07-16
Publishing country	India
Document type	Journal Article
ZDB-ID	2596956-0
ISSN	2213-3445 ; 0976-5662
ISSN (online)	2213-3445
ISSN	0976-5662
DOI	10.1016/j.jcot.2023.102226
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Article ; Online: Transcription factor mce3R modulates antibiotics and disease persistence in Mycobacteriumtuberculosis.

Pandey, Manitosh / Talwar, Sakshi / Pal, Rahul / Nain, Vaibhav / Johri, Sonia / Singhal, Amit / Pandey, Amit Kumar

Research in microbiology

2023 Volume 174, Issue 7, Page(s) 104082

Abstract: Transcription factors (TFs) of Mycobacterium tuberculosis (Mtb), an etiological agent of tuberculosis, regulate a network of pathways that help prolong the survival of Mtb inside the host. In this study, we have characterized a transcription repressor ... ...

Abstract	Transcription factors (TFs) of Mycobacterium tuberculosis (Mtb), an etiological agent of tuberculosis, regulate a network of pathways that help prolong the survival of Mtb inside the host. In this study, we have characterized a transcription repressor gene (mce3R) from the TetR family, that encodes for Mce3R protein in Mtb. We demonstrated that the mce3R gene is dispensable for the growth of Mtb on cholesterol. Gene expression analysis suggests that the transcription of genes belonging to the mce3R regulon is independent of the carbon source. We found that, in comparison to the wild type, the mce3R deleted strain (Δmce3R) generated more intracellular ROS and demonstrated reduced susceptibility to oxidative stress. Total lipid analysis suggests that mce3R regulon encoded proteins modulate the biosynthesis of cell wall lipids in Mtb. Interestingly, the absence of Mce3R increased the frequency of generation of antibiotic persisters in Mtb and imparted in-vivo growth advantage phenotype in guinea pigs. In conclusion, genes belonging to the mce3R regulon modulate the frequency of generation of persisters in Mtb. Hence, targeting mce3R regulon encoded proteins could potentiate the current regimen by eliminating persisters during Mtb infection.
Language	English
Publishing date	2023-05-25
Publishing country	France
Document type	Journal Article
ZDB-ID	1004220-9
ISSN	1769-7123 ; 0923-2508
ISSN (online)	1769-7123
ISSN	0923-2508
DOI	10.1016/j.resmic.2023.104082
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Article ; Online: A multi-modal assessment of sleep stages using adaptive Fourier decomposition and machine learning.

Fatimah, Binish / Singhal, Amit / Singh, Pushpendra

Computers in biology and medicine

2022 Volume 148, Page(s) 105877

Abstract: Healthy sleep is essential for the rejuvenation of the body and helps in maintaining good health. Many people suffer from sleep disorders that are characterized by abnormal sleep patterns. Automated assessment of such disorders using biomedical signals ... ...

Abstract	Healthy sleep is essential for the rejuvenation of the body and helps in maintaining good health. Many people suffer from sleep disorders that are characterized by abnormal sleep patterns. Automated assessment of such disorders using biomedical signals has been an active subject of research. Electroencephalogram (EEG) is a popular diagnostic used in this regard. We consider a widely-used publicly available database and process the signals using the Fourier decomposition method (FDM) to obtain narrowband signal components. Statistical features extracted from these components are passed on to machine learning classifiers to identify different stages of sleep. A novel feature measuring the non-stationarity of the signal is also used to capture salient information. It is shown that classification results can be improved by using multi-channel EEG instead of single-channel EEG data. Simultaneous utilization of multiple modalities, such as Electromyogram (EMG), Electrooculogram (EOG) along with EEG data leads to further enhancement in the obtained results. The proposed method can be efficiently implemented in real-time using fast Fourier transform (FFT), and it provides better classification results than the other algorithms existing in the literature. It can assist in the development of low-cost sensor-based setups for continuous patient monitoring and feedback.
MeSH term(s)	Electroencephalography ; Electrooculography ; Humans ; Machine Learning ; Polysomnography ; Signal Processing, Computer-Assisted ; Sleep Stages
Language	English
Publishing date	2022-07-14
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.2022.105877
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Article ; Online: Rv0495c regulates redox homeostasis in Mycobacterium tuberculosis.

Pal, Rahul / Talwar, Sakshi / Pandey, Manitosh / Nain, Vaibhav Kumar / Sharma, Taruna / Tyagi, Shaifali / Barik, Vishawjeet / Chaudhary, Shweta / Gupta, Sonu Kumar / Kumar, Yashwant / Nanda, Ranjan / Singhal, Amit / Pandey, Amit Kumar

Tuberculosis (Edinburgh, Scotland)

2024 Volume 145, Page(s) 102477

Abstract: Mycobacterium tuberculosis (Mtb) has evolved sophisticated surveillance mechanisms to neutralize the ROS-induces toxicity which otherwise would degrade a variety of biological molecules including proteins, nucleic acids and lipids. In the present study, ... ...

Abstract	Mycobacterium tuberculosis (Mtb) has evolved sophisticated surveillance mechanisms to neutralize the ROS-induces toxicity which otherwise would degrade a variety of biological molecules including proteins, nucleic acids and lipids. In the present study, we find that Mtb lacking the Rv0495c gene (ΔRv0495c) is presented with a highly oxidized cytosolic environment. The superoxide-induced lipid peroxidation resulted in altered colony morphology and loss of membrane integrity in ΔRv0495c. As a consequence, ΔRv0495c demonstrated enhanced susceptibility when exposed to various host-induced stress conditions. Further, as expected, we observed a mutant-specific increase in the abundance of transcripts that encode proteins involved in antioxidant defence. Surprisingly, despite showing a growth defect phenotype in macrophages, the absence of the Rv0495c enhanced the pathogenicity and augmented the ability of the Mtb to grow inside the host. Additionally, our study revealed that Rv0495c-mediated immunomodulation by the pathogen helps create a favorable niche for long-term survival of Mtb inside the host. In summary, the current study underscores the fact that the truce in the war between the host and the pathogen favours long-term disease persistence in tuberculosis. We believe targeting Rv0495c could potentially be explored as a strategy to potentiate the current anti-TB regimen.
MeSH term(s)	Humans ; Mycobacterium tuberculosis ; Bacterial Proteins/metabolism ; Tuberculosis/microbiology ; Oxidation-Reduction ; Homeostasis/physiology
Chemical Substances	Bacterial Proteins
Language	English
Publishing date	2024-01-06
Publishing country	Scotland
Document type	Journal Article
ZDB-ID	2046804-0
ISSN	1873-281X ; 1472-9792
ISSN (online)	1873-281X
ISSN	1472-9792
DOI	10.1016/j.tube.2024.102477
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