Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation

The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart's rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients' acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11\%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.Comment: 14 pages, 5 figures, accepted for future publication in Remote Sensing MDPI Journa

Anterior Cervical Corpectomy with Cage Fixation for Cervical Spondylotic Myelopathy

Objective:  This study aimed to report the clinical outcome of anterior cervical corpectomy with cage fixation in patients with cervical spondylotic myelopathy. Material and Methods:  This observational retrospective study included 36 patients from the Neurosurgery department of Lady Ready Hospital MTI, Peshawar from 2014 January to 2015 December. After performing surgery, the patients were followed up for six months for neurological outcome and various post-operative complications such as infection, transient recurrent laryngeal palsy, screw displacement and improvements in paresthesias and gait ataxia. Results:  Most of the patients have no post-operative complications. Seventy-five percent (n = 27) of patients reported an immediate improvement in paresthesia and fine hand movements and gait. The major reported complications were implant failure (5.55%) and recurrent laryngeal nerve transient palsy in two patients (5.55%) each. Conclusion:  In patients with cervical spondylotic myelopathy, with anterior compression, cervical corpectomy with cage fixation is less invasive and an effective procedure with acceptable outcomes

Upper Limb Movement Execution Classification using Electroencephalography for Brain Computer Interface

An accurate classification of upper limb movements using electroencephalography (EEG) signals is gaining significant importance in recent years due to the prevalence of brain-computer interfaces. The upper limbs in the human body are crucial since different skeletal segments combine to make a range of motion that helps us in our trivial daily tasks. Decoding EEG-based upper limb movements can be of great help to people with spinal cord injury (SCI) or other neuro-muscular diseases such as amyotrophic lateral sclerosis (ALS), primary lateral sclerosis, and periodic paralysis. This can manifest in a loss of sensory and motor function, which could make a person reliant on others to provide care in day-to-day activities. We can detect and classify upper limb movement activities, whether they be executed or imagined using an EEG-based brain-computer interface (BCI). Toward this goal, we focus our attention on decoding movement execution (ME) of the upper limb in this study. For this purpose, we utilize a publicly available EEG dataset that contains EEG signal recordings from fifteen subjects acquired using a 61-channel EEG device. We propose a method to classify four ME classes for different subjects using spectrograms of the EEG data through pre-trained deep learning (DL) models. Our proposed method of using EEG spectrograms for the classification of ME has shown significant results, where the highest average classification accuracy (for four ME classes) obtained is 87.36%, with one subject achieving the best classification accuracy of 97.03%

Frequency and Pattern of Early Complications after Endoscopic Third Ventriculostomy in Obstructive Hydrocephalus

Objective:  To determine the frequency, pattern and outcome of early complications after endoscopic third ventriculostomy (ETV) in Obstructive hydrocephalus. Material and Methods:  The study included 160 patients from Neurosurgery department, Lady Reading Hospital Peshawar and private clinics over a period of twelve months. After performing ETV under general anesthesia by a single expert neurosurgeon, the patients were followed up for seven days post operatively for the CSF leak, wound infection, meningitis, seizures, bleeding and in hospital death. Results:  Eighty five percent of the patients had no untoward complications, while 15% showed complications including CSF leak (5%), wound infection (3%), meningitis (2%), seizures (2%), bleeding (2%) and in hospital death (1%). Conclusion:  Due to the less invasive nature, endoscopic third ventriculostomy is favored for treating obstructive hydrocephalus in select patient population as it is safe and have better outcomes

Personality Trait Recognition using ECG Spectrograms and Deep Learning

This paper presents an innovative approach to recognizing personality traits using deep learning (DL) methods applied to electrocardiogram (ECG) signals. Within the framework of detecting the big five personality traits model encompassing extra-version, neuroticism, agreeableness, conscientiousness, and openness, the research explores the potential of ECG-derived spectrograms as informative features. Optimal window sizes for spectrogram generation are determined, and a convolutional neural network (CNN), specifically Resnet-18, and visual transformer (ViT) are employed for feature extraction and personality trait classification. The study utilizes the publicly available ASCERTAIN dataset, which comprises various physiological signals, including ECG recordings, collected from 58 participants during the presentation of video stimuli categorized by valence and arousal levels. The outcomes of this study demonstrate noteworthy performance in personality trait classification, consistently achieving F1-scores exceeding 0.9 across different window sizes and personality traits. These results emphasize the viability of ECG signal spectrograms as a valuable modality for personality trait recognition, with Resnet-18 exhibiting effectiveness in discerning distinct personality traits

Clinical severity spectrum of re-infected cov-19 patients in Khyber Pakhtunkhuwa (KPK)

Background: Re-infection is infection likely to occurred second time. Recently due to increase in the pandemic of SARS COV-2 there were increased in cases of re-infection but the clinically severity spectrum of re-infection is unknown. This study is design to determine clinical severity of re-infected COV-19 patients. Material and methodology: This comparative cross-sectional study was conducted in pathology department of Rehman Medical Institute Peshawar Pakistan and Leady Reading Hospital Peshawar within duration of 6 months (February-July 2021). Inclusion criteria: Patients who were re-infected by SARS COV-2 and having duration between primary infection and re-infection are > 30 days were included. Exclusion criteria: Unwilling patient to give data and patients having duration between primary infection and re-infection is<30 days were excluded from the study. Sample size: Total 32 samples were collected by designing proper Questionnaire according to the criteria of (World Health Organization). The collected data will be analyzed through SPSS version 22. The frequencies, mean, standard deviation of data was performed by descriptive statistics in SPSS. Result: Out of 32 primary SARS COV-2 infected individuals, 20 have mild infection while in same 32 re-infected SARS COV-2 individuals 19 individuals have severe symptoms in its secondary infection with mean age (32years) of individuals. Conclusion: In current study by comparing primary symptoms of SARS COV-2 infected individuals with symptoms of Re-infected individuals, symptoms are mild and severe respectively. Hence, re-infection cause by SARS COV-2 is more severe than primary infectio

Reliability and Validity of Scales Assessing Anxiety Associated with Information Related Tasks: A Systematic Review

This research carried out a systematic review of the evidence of reliability and validity of scales available in studies reporting surveys of individuals to assess anxiety associated with information related tasks such as library anxiety, information seeking anxiety, and information anxiety. A systematic search using keywords ‘library anxiety’, ‘information anxiety’, \u27information seeking anxiety\u27, and \u27information seeking\u27 AND \u27anxiety\u27 was carried in Web of Science, Scopus, LISA, and LISTA to identify the relevant literature. This review included those studies reporting the use of any scale assessing information related anxiety, and published in the English language, and included all type of documents (e.g. journal articles, conference papers, book chapters, thesis/dissertations, reports). The two-phase screening process, title/abstract screening, and full-text screening resulted in 85 eligible studies reviewed in this paper. The data extracted from each eligible study included author names, year of publication, scale title, type of constructed assessed, sample characteristics, number of items in the scale, types of reliability and validity reported. The results revealed that most of the empirical studies did not report the reliability and validity of scales used for data collection. Eight instruments assessing information related anxieties were identified. These scales were heterogeneous in the number of statements and subscales and homogenous in the type of scale options. An internal consistency coefficient such as Cronbach\u27s alpha was the widely used reliability measure. Face validity, content validity, and construct validity either through exploratory factor analysis or confirmatory factor analysis were the most used validity measures. These results quite had serious implications on the inferences drawn by the practitioners and researchers based on the results of existing studies. The use of good-quality measures for assessing information related anxieties needs to be promoted not only by academicians but also by journal referees and editors. This review would be a worthy contribution in the existing research on information related anxieties as no such study appeared so far in this area

Densely Deformable Efficient Salient Object Detection Network

Salient Object Detection (SOD) domain using RGB-D data has lately emerged with some current models' adequately precise results. However, they have restrained generalization abilities and intensive computational complexity. In this paper, inspired by the best background/foreground separation abilities of deformable convolutions, we employ them in our Densely Deformable Network (DDNet) to achieve efficient SOD. The salient regions from densely deformable convolutions are further refined using transposed convolutions to optimally generate the saliency maps. Quantitative and qualitative evaluations using the recent SOD dataset against 22 competing techniques show our method's efficiency and effectiveness. We also offer evaluation using our own created cross-dataset, surveillance-SOD (S-SOD), to check the trained models' validity in terms of their applicability in diverse scenarios. The results indicate that the current models have limited generalization potentials, demanding further research in this direction. Our code and new dataset will be publicly available at https://github.com/tanveer-hussain/EfficientSO