26 research outputs found
Explorative survey on the usage and needs of Mobile Health Applications (mhealth) amongst caregivers in taking care of stroke survivors
Mobile health (mHealth) applications may assist stroke caregivers in answering the unmet needs of inadequate information and support from healthcare professionals. This study aimed to explore the usage and needs of mHealth applications among stroke caregivers and its associated factors. This cross-sectional study involved 207 stroke caregivers attending hospital and community-based stroke clinics in Kuala Lumpur between December 2020 until March 2021. The survey was done through newly developed self-administered bilingual questionnaires using face to face interviews, telephone interviews and Google form. Most caregivers (77.8%) used at least one mHealth application for self-care or during the caregiving process. The three most types of mHealth applications were contacting healthcare professionals (89.9%), disease monitoring (89.4%) and health information (89.4%). The three most features of mHealth applications were free to download/use (94.7%), simple interface (93.7%) and data security/privacy (93.7%). Chi-square test of association showed managing appointment (χ2 (1) = 5.65, p = 0.017), health information (χ2 (1) = 6.72, p = 0.01), disease monitoring (χ2 (1) = 9.58, p = 0.002), contacting healthcare professional (χ2 (1) = 6.27, p = 0.012) and patient disability level (χ2 (1) = 4.29, p = 0.038) were significantly associated with specific types of mHealth applications. In conclusion, the overall usage of mHealth applications among stroke caregivers was high, with the need of specific types and features in mHealth applications among stroke caregivers
Neuroleptic malignant syndrome in an elderly patient with bipolar disorder
Neuroleptic malignant syndrome (NMS) is a well-known and potentially fatal
complication of antipsychotic use. The elderly population, with multiple risk
factors, are more vulnerable to this condition. We described a case of an 80-year-old man with bipolar disorder, previously on oral extended-release sodium
valproate, aripiprazole and long-acting injectable paliperidone, who developed
NMS. He presented with generalised muscle rigidity, fever, fluctuating blood
pressure and elevated creatinine kinase during his hospitalisation for a manic
episode. Contributing factors included old age, underlying vascular Parkinsonism,
electrolyte imbalance, intercurrent lung infection with acute exacerbation of chronic
obstructive pulmonary disease, hyperactive delirium, and repeated administration
of parenteral typical antipsychotic. Antipsychotics were withheld promptly, and the
patient was treated with dantrolene, bromocriptine and amantadine. His symptoms
resolved after a week. He subsequently remained well with oral extended-release
sodium valproate alone. Relevant clinical points are discussed. Clinical vigilance,
close interdisciplinary cooperation, and prompt interventions are keys to successful
to management of NMS in elderly patients
Successful intravenous thrombolysis of a wake-up stroke with underlying valvular atrial fibrillation
A 42-year-old female admitted with new-onset atrial fibrillation had a wake-up stroke on the high-dependency unit and the time last seen well (TLSW) was 6.5 h. She suffered left-sided body weakness and her National Institutes of Health Stroke Scale (NIHSS) score was 17. An emergency CT perfusion showed right M1 segment occlusion with more than 50% penumbra. She was given recombinant tissue plasminogen activator (r-tPA) at 9 h from TLSW. An immediate diagnostic angiogram with intention to treat, owing to the presence of large vessel occlusion, showed complete reperfusion after intravenous r-tPA. She was discharged with NIHSS of 2, and at 3-month follow up her Modified Rankin Scale was 0. We demonstrated a successful reperfusion and excellent clinical recovery with intravenous thrombolysis in a patient who presented with a wake-up stroke with underlying valvular atrial fibrillation despite evidence of large vessel occlusion. © 2018, Royal College of Physicians of Edinburgh. All rights reserved
A Classification Model of EEG Signals Based on RNN-LSTM for Diagnosing Focal and Generalized Epilepsy
Epilepsy is a chronic neurological disorder caused by abnormal neuronal activity that is diagnosed visually by analyzing electroencephalography (EEG) signals. Background: Surgical operations are the only option for epilepsy treatment when patients are refractory to treatment, which highlights the role of classifying focal and generalized epilepsy syndrome. Therefore, developing a model to be used for diagnosing focal and generalized epilepsy automatically is important. Methods: A classification model based on longitudinal bipolar montage (LB), discrete wavelet transform (DWT), feature extraction techniques, and statistical analysis in feature selection for RNN combined with long short-term memory (LSTM) is proposed in this work for identifying epilepsy. Initially, normal and epileptic LB channels were decomposed into three levels, and 15 various features were extracted. The selected features were extracted from each segment of the signals and fed into LSTM for the classification approach. Results: The proposed algorithm achieved a 96.1% accuracy, a 96.8% sensitivity, and a 97.4% specificity in distinguishing normal subjects from subjects with epilepsy. This optimal model was used to analyze the channels of subjects with focal and generalized epilepsy for diagnosing purposes, relying on statistical parameters. Conclusions: The proposed approach is promising, as it can be used to detect epilepsy with satisfactory classification performance and diagnose focal and generalized epilepsy
Evaluating improvement in acute stroke management following pre-hospital initiation of acute stroke service
Prehospital notification of the stroke team in alerting incoming acute stroke patient
has been practiced in several countries worldwide. Currently this is not practiced in
Malaysia. This study evaluates feasibility and impact to stroke team door to review
time when prehospital notification is employed. Duration of case control study
was between June 2018 to January 2019. Control phase consists of conventionally
activating stroke team after in-hospital assessment by emergency medical officer.
This was then followed by an intervention phase where on scene activation of
stroke team was done by the Prehospital Emergency Care (PHC) staff. Training of
PHC staff in recognising an acute stroke was based on identification of BE-FAST
(Balance, Eyes, Face, Arm and Speech Test) abnormalities. The objectives were to
compare the mean between two groups for acute stroke team review time, door
to computerised tomography (CT) scan and door to thrombolysis time. Thirty-nine
patients were analysed (control n=29, intervention n=10). Results were insignificant
(p>0.05). Mean time in minutes for control phase vs. intervention phase was as
follows: Door to stroke team review time, 25.96 + 39.16 vs. 15.9 + 13.14, door to CT
scan was 43.04 + 40.00 vs. 25.8 + 11.35. Only 3 patients underwent thrombolytic
therapy during study period. Limitation was non-parametric data with lack of
number of acute stroke cases responded during the intervention period. With
continual training of pre-hospital staff in detecting acute stroke, feasibility can be
improved
Brain Dynamics in Response to Intermittent Photic Stimulation in Epilepsy
Purpose: Routine electroencephalogram (EEG) examinations uses intermittent photic stimulation (IPS) for investigation of the visual cortex EEG responses during resting time. This study aimed to discover brain dynamics effects of IPS in 28 generalized epilepsy patients and 28 healthy subjects. Methodology: Signal processing techniques were used in feature extraction by Fast Fourier transform (FFT), feature dimension reduction by t-test (significant, p<0.05) and classification by nearest neighbor (k-NN) and support vector machine (SVM). Results: The epilepsy group had higher level of amplitude in Theta waves compared to the healthy group. The Alpha waves in the resting time and for all IPS frequencies were observed with lower level of amplitude in healthy subjects compared to the epilepsy group. The k-NN (85.7% accuracy) classifier had the best discrimination of epilepsy from healthy group for resting time versus during IPS at 18 Hz IPS. However, using SVM (75.0% accuracy), IPS at 25 Hz yielded the best discrimination between resting time versus IPS in epilepsy where the healthy group responded similarly in all IPS frequencies. Conclusions: This study shows that IPS at 18 Hz and 25 Hz are suitable IPS frequencies for k-NN and SVM, respectively, to discriminate non-photosensitive generalized epilepsy from normal subjects during interictal
Assessment of a 16-Channel Ambulatory Dry Electrode EEG for Remote Monitoring
Ambulatory EEGs began emerging in the healthcare industry over the years, setting a new norm for long-term monitoring services. The present devices in the market are neither meant for remote monitoring due to their technical complexity nor for meeting clinical setting needs in epilepsy patient monitoring. In this paper, we propose an ambulatory EEG device, OptiEEG, that has low setup complexity, for the remote EEG monitoring of epilepsy patients. OptiEEG’s signal quality was compared with a gold standard clinical device, Natus. The experiment between OptiEEG and Natus included three different tests: eye open/close (EOC); hyperventilation (HV); and photic stimulation (PS). Statistical and wavelet analysis of retrieved data were presented when evaluating the performance of OptiEEG. The SNR and PSNR of OptiEEG were slightly lower than Natus, but within an acceptable bound. The standard deviations of MSE for both devices were almost in a similar range for the three tests. The frequency band energy analysis is consistent between the two devices. A rhythmic slowdown of theta and delta was observed in HV, whereas photic driving was observed during PS in both devices. The results validated the performance of OptiEEG as an acceptable EEG device for remote monitoring away from clinical environments
Knowledge of Acute Stroke Management Among Healthcare Professionals: Development and Validation of Acute Stroke Management Questionnaire (ASMaQ)
Background: Around 15.0% of all strokes occurred in hospitalised patients and studies
showed significant delay in the stroke recognition and lack of awareness on
thrombolytic therapy for acute ischaemic stroke (AIS) which lead to higher mortality
for in-hospital stroke. We aimed to develop and validate a new instrument
known as acute stroke management questionnaire (ASMaQ) to evaluate the awareness
of healthcare professionals in managing acute ischaemic stroke cases.
Methods: This study consisted of 3 steps; the formulation of ASMaQ draft, content
validation and construct validity. A total of 110 questions were drafted with 5-point
Likert scale answers. From the list, 31 were selected and subsequently tested on 158
participants. The results were analysed and validated using exploratory factor analysis
on SPSS. Components were extracted and questions with low factor loading
were removed. The internal consistency was then measured with Cronbach’s alpha.
Results: Following analysis, 3 components were extracted and named as general
stroke knowledge, hyperacute stroke care and advanced stroke management. Two
items were deleted leaving 29 out of 31 questions for the final validated ASMaQ.
Internal consistency showed high reliability with Cronbach’s alpha of 0.82. Our
respondents scored a total cumulative mean of 113.62 marks or 66.6%. A sub analysis
by occupation showed that medical assistants scored the lowest in the group
with a score of 57% whilst specialists including neurologists scored the highest at
79.4%. Conclusion: The ASMaQ is a newly developed and validated questionnaire
consisting of 29 questions testing the respondents’ acute stroke management
knowledge
T100. Computerized Recognition of Epileptic Discharge (CREED) algorithm and its clinical application
Direct Medical Cost of Stroke and the Cost-Effectiveness of Direct Oral Anticoagulants in Atrial Fibrillation-Related Stroke: A Cross-Sectional Study
Background: Stroke has significant direct medical costs, and direct oral anticoagulants (DOACs) are better alternatives to warfarin for stroke prevention in atrial fibrillation (AF). This study aimed to determine the direct medical costs of stroke, with emphasis on AF stroke and the cost-effectiveness of DOACs among stroke patients in a tertiary hospital in Malaysia. Methods: This study utilised in-patient data from the case mix unit of Universiti Kebangsaan Malaysia Medical Centre (UKMMC) between 2011 and 2018. Direct medical costs of stroke were determined using a top-down costing approach and factors associated with costs were identified. Incremental cost effectiveness ratio (ICER) was calculated to compare the cost-effectiveness between DOACs and warfarin. Results: The direct medical cost of stroke was MYR 11,669,414.83 (n = 3689). AF-related stroke cases had higher median cost of MYR 2839.73 (IQR 2269.79–3101.52). Regression analysis showed that stroke type (AF versus non-AF stroke) (p = 0.013), stroke severity (p = 0.010) and discharge status (p < 0.001) significantly influenced stroke costs. DOACs were cost-effective compared to warfarin with an ICER of MYR 19.25. Conclusions: The direct medical cost of stroke is substantial, with AF-stroke having a higher median cost per stroke care. DOACs were cost effective in the treatment of AF-related stroke in UKMMC