KU-BdSL: An open dataset for Bengali sign language recognition.

Sign language is a form of communication medium for speech and hearing disabled people. It has various forms with different troublesome patterns, which are difficult for the general mass to comprehend. Bengali sign language (BdSL) is one of the difficult sign languages due to its immense number of alphabet, words, and expression techniques. Machine translation can ease the difficulty for disabled people to communicate with generals. From the machine learning (ML) domain, computer vision can be the solution for them, and every ML solution requires a optimized model and a proper dataset. Therefore, in this research work, we have created a BdSL dataset and named `KU-BdSL', which consists of 30 classes describing 38 consonants ('banjonborno') of the Bengali alphabet. The dataset includes 1500 images of hand signs in total, each representing Bengali consonant(s). Thirty-nine participants (30 males and 9 females) of different ages (21-38 years) participated in the creation of this dataset. We adopted smartphones to capture the images due to the availability of their high-definition cameras. We believe that this dataset can be beneficial to the deaf and dumb (D&D) community. Identification of Bengali consonants of BdSL from images or videos is feasible using the dataset. It can also be employed for a human-machine interface for disabled people. In the future, we will work on the vowels and word level of BdSL.

A Bibliometric Analysis on Arrhythmia Detection and Classification from 2005 to 2022.

Bibliometric analysis is a widely used technique for analyzing large quantities of academic literature and evaluating its impact in a particular academic field. In this paper bibliometric analysis has been used to analyze the academic research on arrhythmia detection and classification from 2005 to 2022. We have followed PRISMA 2020 framework to identify, filter and select the relevant papers. This study has used the Web of Science database to find related publications on arrhythmia detection and classification. "Arrhythmia detection", "arrhythmia classification" and "arrhythmia detection and classification" are three keywords for gathering the relevant articles. 238 publications in total were selected for this research. In this study, two different bibliometric techniques, "performance analysis" and "science mapping", were applied. Different bibliometric parameters such as publication analysis, trend analysis, citation analysis, and networking analysis have been used to evaluate the performance of these articles. According to this analysis, the three countries with the highest number of publications and citations are China, the USA, and India in terms of arrhythmia detection and classification. The three most significant researchers in this field are those named U. R. Acharya, S. Dogan, and P. Plawiak. Machine learning, ECG, and deep learning are the three most frequently used keywords. A further finding of the study indicates that the popular topics for arrhythmia identification are machine learning, ECG, and atrial fibrillation. This research provides insight into the origins, current status, and future direction of arrhythmia detection research.

A Smartphone-Based Model of Care to Support Patients With Cardiac Disease Transitioning From Hospital to the Community (TeleClinical Care): Pilot Randomized Controlled Trial.

BACKGROUND: Patients hospitalized with acute coronary syndrome (ACS) or heart failure (HF) are frequently readmitted. This is the first randomized controlled trial of a mobile health intervention that combines telemonitoring and education for inpatients with ACS or HF to prevent readmission. OBJECTIVE: This study aims to investigate the feasibility, efficacy, and cost-effectiveness of a smartphone app-based model of care (TeleClinical Care [TCC]) in patients discharged after ACS or HF admission. METHODS: In this pilot, 2-center randomized controlled trial, TCC was applied at discharge along with usual care to intervention arm participants. Control arm participants received usual care alone. Inclusion criteria were current admission with ACS or HF, ownership of a compatible smartphone, age ≥18 years, and provision of informed consent. The primary end point was the incidence of unplanned 30-day readmissions. Secondary end points included all-cause readmissions, cardiac readmissions, cardiac rehabilitation completion, medication adherence, cost-effectiveness, and user satisfaction. Intervention arm participants received the app and Bluetooth-enabled devices for measuring weight, blood pressure, and physical activity daily plus usual care. The devices automatically transmitted recordings to the patients' smartphones and a central server. Thresholds for blood pressure, heart rate, and weight were determined by the treating cardiologists. Readings outside these thresholds were flagged to a monitoring team, who discussed salient abnormalities with the patients' usual care providers (cardiologists, general practitioners, or HF outreach nurses), who were responsible for further management. The app also provided educational push notifications. Participants were followed up after 6 months. RESULTS: Overall, 164 inpatients were randomized (TCC: 81/164, 49.4%; control: 83/164, 50.6%; mean age 61.5, SD 12.3 years; 130/164, 79.3% men; 128/164, 78% admitted with ACS). There were 11 unplanned 30-day readmissions in both groups (P=.97). Over a mean follow-up of 193 days, the intervention was associated with a significant reduction in unplanned hospital readmissions (21 in TCC vs 41 in the control arm; P=.02), including cardiac readmissions (11 in TCC vs 25 in the control arm; P=.03), and higher rates of cardiac rehabilitation completion (20/51, 39% vs 9/49, 18%; P=.03) and medication adherence (57/76, 75% vs 37/74, 50%; P=.002). The average usability rating for the app was 4.5/5. The intervention cost Aus $6028 (US $4342.26) per cardiac readmission saved. When modeled in a mainstream clinical setting, enrollment of 237 patients was projected to have the same expenditure compared with usual care, and enrollment of 500 patients was projected to save approximately Aus $100,000 (approximately US $70,000) annually. CONCLUSIONS: TCC was feasible and safe for inpatients with either ACS or HF. The incidence of 30-day readmissions was similar; however, long-term benefits were demonstrated, including fewer readmissions over 6 months, improved medication adherence, and improved cardiac rehabilitation completion. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12618001547235; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=375945.

Trials and Tribulations: mHealth Clinical Trials in the COVID-19 Pandemic.

INTRODUCTION: Mobile phone-based interventions in cardiovascular disease are growing in popularity. A randomised control trial (RCT) for a novel smartphone app-based model of care, named TeleClinical Care - Cardiac (TCC-Cardiac), commenced in February 2019, targeted at patients being discharged after care for an acute coronary syndrome or episode of decompensated heart failure. The app was paired to a digital sphygmomanometer, weighing scale and a wearable fitness band, all loaned to the patient, and allowed clinicians to respond to abnormal readings. The onset of the COVID-19 pandemic necessitated several modifications to the trial in order to protect participants from potential exposure to infection. The use of TCC-Cardiac during the pandemic inspired the development of a similar model of care (TCC-COVID), targeted at patients being managed at home with a diagnosis of COVID-19. METHODS: Recruitment for the TCC-Cardiac trial was terminated shortly after the World Health Organization announced COVID-19 as a global pandemic. Telephone follow-up was commenced, in order to protect patients from unnecessary exposure to hospital staff and patients. Equipment was returned or collected by a 'no-contact' method. The TCC-COVID app and model of care had similar functionality to the original TCC-Cardiac app. Participants were enrolled exclusively by remote methods. Oxygen saturation and pulse rate were measured by a pulse oximeter, and symptomatology measured by questionnaire. Measurement results were manually entered into the app and transmitted to an online server for medical staff to review. RESULTS: A total of 164 patients were involved in the TCC-Cardiac trial, with 102 patients involved after the onset of the pandemic. There were no hospitalisations due to COVID-19 in this cohort. The study was successfully completed, with only three participants lost to follow-up. During the pandemic, 5 of 49 (10%) of patients in the intervention arm were readmitted compared to 12 of 53 (23%) in the control arm. Also, in this period, 28 of 29 (97%) of all clinically significant alerts received by the monitoring team were managed successfully in the outpatient setting, avoiding hospitalisation. Patients found the user experience largely positive, with the average rating for the app being 4.56 out of 5. 26 patients have currently been enrolled for TCC-COVID. Recruitment is ongoing. All patients have been safely and effectively monitored, with no major adverse clinical events or technical malfunctions. Patient satisfaction has been high. CONCLUSION: The TCC-Cardiac RCT was successfully completed despite the challenges posed by COVID-19. Use of the app had an added benefit during the pandemic as participants could be monitored safely from home. The model of care inspired the development of an app with similar functionality designed for use with patients diagnosed with COVID-19.

Process Evaluation of a Randomised Controlled Trial for TeleClinical Care, a Smartphone-App Based Model of Care.

BACKGROUND: A novel smartphone app-based model of care (TeleClinical Care - TCC) for patients with acute coronary syndrome (ACS) and heart failure (HF) was evaluated in a two-site, pilot randomised control trial of 164 participants in Sydney, Australia. The program included a telemonitoring system whereby abnormal blood pressure, weight and heart rate readings were monitored by a central clinical team, who subsequently referred clinically significant alerts to the patients' usual general practitioner (GP, also known as primary care physician in the United States), HF nurse or cardiologist. While the primary endpoint, 30-day readmissions, was neutral, intervention arm participants demonstrated improvements in readmission rates over 6 months, cardiac rehabilitation (CR) completion and medication compliance. A process evaluation was designed to identify contextual factors and mechanisms that influenced the results, as well as strategies of improving site and participant recruitment and the delivery of the intervention, for a planned larger effectiveness trial of over 1,000 patients across the state of New South Wales, Australia (TCC-Cardiac). METHODS: Multiple data sources were used in this mixed-methods process evaluation, including interviews with four TCC team members, three GPs and three cardiologists. CR completion rates, HF outreach service (HFOS) referrals and cardiologist follow-up appointments were audited. A patient questionnaire was also analysed for evidence of improved self-care as a hypothesised mechanism of the TCC app. An implementation research logic model was used to synthesise our findings. RESULTS: Rates of HFOS referral (83 vs. 72%) and cardiologist follow-up (96 vs. 93%) were similarly high in the intervention and control arms, respectively. Team members were largely positive towards their orientation and training, but highlighted several implementation strategies that could be optimised for TCC-Cardiac: streamlining of the enrolment process, improving the reach of the trial by screening patients in non-cardiac wards, and ensuring team members had adequate time to recruit (>15 h per week). GPs and cardiologists viewed the intervention acceptably regarding potential benefit of closely monitoring, and responding to abnormalities for their patients, though there were concerns of the potential additional workload generated by alerts that did not merit clinical intervention. Clear delineation of which clinician (GP or cardiologist) was primarily responsible for alert management was also recommended, as well as a preference to receive regular summary data. Several patients commented on the mechanisms of improved self-management because of TCC, which could have led to the outcome of improved medication compliance. DISCUSSION: Use of TCC was associated with several benefits, including higher patient engagement and completion rates with CR. The conduct and delivery of TCC-Cardiac will be improved by the findings of this process evaluation to optimise recruitment, and establishing the roles of GPs and cardiologists as part of the model.