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Fulltext An Automated High-Accuracy Detection Scheme for Myocardial Ischemia Based on Multi-Lead Long-Interval ECG and Choi-Williams Time-Frequency Analysis Incorporating a Multi-Class SVM Classifier

Hussein AF, Hashim SJ, Rokhani FZ, Wan Adnan WA

Sensors (Basel), 2021 Mar 26;21(7).
PMID: 33810211 DOI: 10.3390/s21072311

Cardiovascular Disease (CVD) is a primary cause of heart problems such as angina and myocardial ischemia. The detection of the stage of CVD is vital for the prevention of medical complications related to the heart, as they can lead to heart muscle death (known as myocardial infarction). The electrocardiogram (ECG) reflects these cardiac condition changes as electrical signals. However, an accurate interpretation of these waveforms still calls for the expertise of an experienced cardiologist. Several algorithms have been developed to overcome issues in this area. In this study, a new scheme for myocardial ischemia detection with multi-lead long-interval ECG is proposed. This scheme involves an observation of the changes in ischemic-related ECG components (ST segment and PR segment) by way of the Choi-Williams time-frequency distribution to extract ST and PR features. These extracted features are mapped to a multi-class SVM classifier for training in the detection of unknown conditions to determine if they are normal or ischemic. The use of multi-lead ECG for classification and 1 min intervals instead of beats or frames contributes to improved detection performance. The classification process uses the data of 92 normal and 266 patients from four different databases. The proposed scheme delivered an overall result with 99.09% accuracy, 99.49% sensitivity, and 98.44% specificity. The high degree of classification accuracy for the different and unknown data sources used in this study reflects the flexibility, validity, and reliability of this proposed scheme. Additionally, this scheme can assist cardiologists in detecting signal abnormality with robustness and precision, and can even be used for home screening systems to provide rapid evaluation in emergency cases.
Fulltext Defining the concepts of a smart nursing home and its potential technology utilities that integrate medical services and are acceptable to stakeholders: a scoping review protocol

Zhao Y, Rokhani FZ, Shariff Ghazali S, Chew BH

BMJ Open, 2021 02 18;11(2):e041452.
PMID: 33602703 DOI: 10.1136/bmjopen-2020-041452

INTRODUCTION: Smart technologies, digital health and eHealth have been shown to enhance institutional elderly care. Because of the rapidly ageing societies, information technologies in geriatric healthcare are urgently needed. A lot of innovation in smart healthcare has occurred in the past decade, and its use in nursing care assessment, daily living activities and service management is yet to be defined. More fundamentally, the concepts, definitions and scopes of a smart nursing home are still vague. Thus, this scoping review aims to examine the extent, range (variety) and nature (characteristics) of evidence on the existing smart concepts and feasible healthcare technologies, types of medical services in nursing home settings and acceptability of a smart nursing home by the elderly people ≥60 years old, their caregivers, nursing home operators and government agencies.
METHODS AND ANALYSIS: This scoping review will be guided by the smart technology adoption behaviours of elder consumers theoretical model (Elderadopt) by Golant and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews. First, we will conduct an internet search for nursing homes and websites and databases related to the stakeholders to retrieve the definitions, concepts and criteria of a smart nursing home (phase 1). Second, we will conduct an additional systematic electronic database search for published articles on any measures of technological feasibility and integration of medical services in nursing home settings and their acceptability by nursing home residents and caregivers (phase 2). The electronic database search will be carried out from 1999 to 30 September 2020 and limited to works published in English and Chinese languages. For phase 2, the selection of literature is further limited to residents of nursing homes aged ≥60 years old with or without medical needs but are not terminally ill or bed-bound. Qualitative data analysis will follow the Framework Methods and thematic analysis using combined inductive and deductive approaches, conducted by at least two reviewers.
ETHICS AND DISSEMINATION: This protocol is registered on osf.io (URL: https://osf.io/qtwz2/). Ethical approval is not necessary as the scoping review is not a primary study, and the information is collected from selected articles that are publicly available sources. All findings will be disseminated at conferences and published in peer-reviewed journals.
Fulltext An RF energy harvester system using UHF micropower CMOS rectifier based on a diode connected CMOS transistor

Shokrani MR, Khoddam M, Hamidon MN, Kamsani NA, Rokhani FZ, Shafie SB

ScientificWorldJournal, 2014;2014:963709.
PMID: 24782680 DOI: 10.1155/2014/963709

This paper presents a new type diode connected MOS transistor to improve CMOS conventional rectifier's performance in RF energy harvester systems for wireless sensor networks in which the circuits are designed in 0.18 μm TSMC CMOS technology. The proposed diode connected MOS transistor uses a new bulk connection which leads to reduction in the threshold voltage and leakage current; therefore, it contributes to increment of the rectifier's output voltage, output current, and efficiency when it is well important in the conventional CMOS rectifiers. The design technique for the rectifiers is explained and a matching network has been proposed to increase the sensitivity of the proposed rectifier. Five-stage rectifier with a matching network is proposed based on the optimization. The simulation results shows 18.2% improvement in the efficiency of the rectifier circuit and increase in sensitivity of RF energy harvester circuit. All circuits are designed in 0.18 μm TSMC CMOS technology.
Performance Evaluation of Time-Frequency Distributions for ECG Signal Analysis

Hussein AF, Hashim SJ, Aziz AFA, Rokhani FZ, Adnan WAW

J Med Syst, 2017 Nov 29;42(1):15.
PMID: 29188389 DOI: 10.1007/s10916-017-0871-8

The non-stationary and multi-frequency nature of biomedical signal activities makes the use of time-frequency distributions (TFDs) for analysis inevitable. Time-frequency analysis provides simultaneous interpretations in both time and frequency domain enabling comprehensive explanation, presentation and interpretation of electrocardiogram (ECG) signals. The diversity of TFDs and specific properties for each type show the need to determine the best TFD for ECG analysis. In this study, a performance evaluation of five TFDs in term of ECG abnormality detection is presented. The detection criteria based on extracted features from most important ECG signal components (QRS) to detect normal and abnormal cases. This is achieved by estimating its energy concentration magnitude using the TFDs. The TFDs analyse ECG signals in one-minute interval instead of conventional time domain approach that analyses based on beat or frame containing several beats. The MIT-BIH normal sinus rhythm ECG database total records of 18 long-term ECG sampled at 128 Hz have been analysed. The tested TFDs include Dual-Tree Wavelet Transform, Spectrogram, Pseudo Wigner-Ville, Choi-Williams, and Born-Jordan. Each record is divided into one-minute slots, which is not considered previously, and analysed. The sample periods (slots) are randomly selected ten minutes interval for each record. This result with 99.44% detection accuracy for 15,735 ECG beats shows that Choi-Williams distribution is most reliable to be used for heart problem detection especially in automated systems that provide continuous monitoring for long time duration.
Fulltext The Expectations and Acceptability of a Smart Nursing Home Model Among Chinese Older Adults and Family Members: A Qualitative Study

Zhao Y, Sazlina SG, Rokhani FZ, Su J, Chew BH

Asian Nurs Res (Korean Soc Nurs Sci), 2023 Oct;17(4):208-218.
PMID: 37661084 DOI: 10.1016/j.anr.2023.08.002

PURPOSE: This study aims to explore the expectations and acceptability of a smart nursing home model among Chinese older adults and their family members based on a scoping review that defines the concept of smart nursing homes.
METHODS: A qualitative case study was employed for this research. Semi-structured, in-depth interviews and focus group discussions were conducted on WeChat. Participants were purposively sampled through snowball sampling in Hainan and Dalian, China. A total of 28 older adults aged 60-75 and six adult children were interviewed until data saturation was achieved, followed by a thematic analysis.
RESULTS: The expectations of smart nursing homes include: 1) quality of care supported by governments and societies; 2) smart technology applications; 3) the presence of a skilled healthcare professional team; 4) access to and scope of basic medical services; and 5) integration of medical services. The acceptability of smart nursing homes included factors such as stakeholders' perceived efficaciousness, usability, and collateral damages of using smart technologies, and the coping process of adoption was influenced by factors such as age, economic status, health status, education, and openness to smart technologies among older adults.
CONCLUSIONS: Chinese older adults and their family members have a positive perception of the smart nursing home model. The qualitative evidence regarding their expectations and acceptability of smart nursing homes contributes valuable insights for a wide range of stakeholders involved in the planning and implementation of smart nursing homes.
Fulltext The expectations and acceptability of a smart nursing home model among Chinese elderly people: A mixed methods study protocol

Zhao Y, Sazlina SG, Rokhani FZ, Su J, Chew BH

PLoS One, 2021;16(8):e0255865.
PMID: 34424931 DOI: 10.1371/journal.pone.0255865

Nursing homes integrated with smart information such as the Internet of Things, cloud computing, artificial intelligence, and digital health could improve not only the quality of care but also benefit the residents and health professionals by providing effective care and efficient medical services. However, a clear concept of a smart nursing home, the expectations and acceptability from the perspectives of the elderly people and their family members are still unclear. In addition, instruments to measure the expectations and acceptability of a smart nursing home are also lacking. The study aims to explore and determine the levels of these expectations, acceptability and the associated sociodemographic factors. This exploratory sequential mixed methods study comprises a qualitative study which will be conducted through a semi-structured interview to explore the expectations and acceptability of a smart nursing home among Chinese elderly people and their family members (Phase I). Next, a questionnaire will be developed and validated based on the results of a qualitative study in Phase I and a preceding scoping review on smart nursing homes by the same authors (Phase II). Lastly, a nationwide survey will be carried out to examine the levels of expectations and acceptability, and the associated sociodemographic factors with the different categories of expectations and acceptability (Phase III). With a better understanding of the Chinese elderly people's expectations and acceptability of smart technologies in nursing homes, a feasible smart nursing home model that incorporates appropriate technologies, integrates needed medical services and business concepts could be formulated and tested as a solution for the rapidly ageing societies in many developed and developing countries.
The expectations and acceptability of a smart nursing home model among Chinese older adults: a mixed methods study

Zhao Y, Sazlina SG, Rokhani FZ, Chinna K, Su J, Chew BH

BMC Nurs, 2024 Jan 13;23(1):40.
PMID: 38218894 DOI: 10.1186/s12912-023-01676-0

BACKGROUND: Smart nursing homes (SNHs) integrate advanced technologies, including IoT, digital health, big data, AI, and cloud computing to optimise remote clinical services, monitor abnormal events, enhance decision-making, and support daily activities for older residents, ensuring overall well-being in a safe and cost-effective environment. This study developed and validated a 24-item Expectation and Acceptability of Smart Nursing Homes Questionnaire (EASNH-Q), and examined the levels of expectations and acceptability of SNHs and associated factors among older adults in China.
METHODS: This was an exploratory sequential mixed methods study, where the qualitative case study was conducted in Hainan and Dalian, while the survey was conducted in Xi'an, Nanjing, Shenyang, and Xiamen. The validation of EASNH-Q also included exploratory and confirmatory factor analyses. Multinomial logistic regression analysis was used to estimate the determinants of expectations and acceptability of SNHs.
RESULTS: The newly developed EASNH-Q uses a Likert Scale ranging from 1 (strongly disagree) to 5 (strongly agree), and underwent validation and refinement from 49 items to the final 24 items. The content validity indices for relevance, comprehensibility, and comprehensiveness were all above 0.95. The expectations and acceptability of SNHs exhibited a strong correlation (r = 0.85, p