Classification of Diabetics with Various Degrees of Autonomic Neuropathy Based on Linear and Nonlinear Features Using Support Vector Machine

Chiu, Chuang-Chien; Yeh, Shoou-Jeng; Li, Tai-Yue

doi:10.1007/978-3-642-13923-9_5

Cited by 3 publications

(9 citation statements)

References 17 publications

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“…In this systematic review, 38 of the included studies investigated healthy population data [ 1 , 13 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ], 46 studied patient populations [ 2 , 3 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , …”

Section: Resultsmentioning

confidence: 99%

“…A total of 14 studies [ 56 , 58 , 60 , 62 , 64 , 65 , 69 , 70 , 71 , 73 , 78 , 82 , 90 , 92 ] utilized time-series analysis techniques, including dynamic relationship analysis methods, TFA, and wavelet analysis. A total of 5 studies [ 59 , 80 , 89 , 95 , 96 ] employed variations in AR time-series models, 8 studies [ 55 , 57 , 72 , 74 , 77 , 81 , 85 , 86 ] utilized machine learning models, and 19 studies [ 2 , 3 , 61 , 63 , 66 , 67 , 68 , 75 , 76 , 79 , 83 , 84 , 87 , 88 , 91 , 93 , 94 , 97 , 98 ] carried out comparative model evaluations.…”

Section: Resultsmentioning

confidence: 99%

“…In the majority of the articles, the study population consisted primarily of traumatic brain injury (TBI) patients with various severities, with the exceptions of articles that studied idiopathic normal-pressure hydrocephalus patients [ 60 ], SAH patients [ 62 , 74 ], cerebrospinal fluid (CSF) infusion patients [ 73 ], coronary intervention patients [ 56 , 81 , 82 ], pediatric patients [ 61 , 78 , 90 ], elderly people with cerebral infarction (CI) [ 64 , 71 ], hypertensive individuals [ 70 ], diabetic patients [ 57 ], intracerebral hemorrhage patients [ 72 , 87 , 88 ], stroke patients [ 66 ], arterial stenosis patients [ 67 , 75 , 84 ], concussion patients [ 68 ], and patients with various ICP-related conditions [ 3 ].…”

Section: Resultsmentioning

confidence: 99%

“…In most of the studies, the data were resampled from very high resolution to low resolution ranging from 0.1 Hz to 5 Hz. Overall, the majority of the studies recorded ICP with either a fiber-optic transducer, a subdural catheter, an intraparenchymal probe, or an external ventricular drain, except for studies that recorded only CBFv via a TCD probe from the MCA [ 56 , 57 , 62 , 66 , 67 , 68 , 73 , 75 , 78 , 81 , 82 , 84 ], Δ[HbO] and Δ[Hb] using NIRS [ 64 , 70 , 71 ], or cerebral tissue oxygen saturation (SctO 2 ) via oximetry [ 90 ]. Some studies estimated CPP from MABP and ICP [ 2 , 55 , 58 , 59 , 61 , 63 , 74 , 80 , 94 , 95 ], pressure–volume reserve (RAP) from the pulse amplitude of ICP and ICP data [ 55 , 65 ], and pressure reactivity index (PRx) from ABP and ICP [ 55 , 69 , 74 , 89 ].…”

Section: Resultsmentioning

confidence: 99%

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Time-Series Modeling and Forecasting of Cerebral Pressure–Flow Physiology: A Scoping Systematic Review of the Human and Animal Literature

Vakitbilir,

Froese,

Gomez

et al. 2024

Sensors

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The modeling and forecasting of cerebral pressure–flow dynamics in the time–frequency domain have promising implications for veterinary and human life sciences research, enhancing clinical care by predicting cerebral blood flow (CBF)/perfusion, nutrient delivery, and intracranial pressure (ICP)/compliance behavior in advance. Despite its potential, the literature lacks coherence regarding the optimal model type, structure, data streams, and performance. This systematic scoping review comprehensively examines the current landscape of cerebral physiological time-series modeling and forecasting. It focuses on temporally resolved cerebral pressure–flow and oxygen delivery data streams obtained from invasive/non-invasive cerebral sensors. A thorough search of databases identified 88 studies for evaluation, covering diverse cerebral physiologic signals from healthy volunteers, patients with various conditions, and animal subjects. Methodologies range from traditional statistical time-series analysis to innovative machine learning algorithms. A total of 30 studies in healthy cohorts and 23 studies in patient cohorts with traumatic brain injury (TBI) concentrated on modeling CBFv and predicting ICP, respectively. Animal studies exclusively analyzed CBF/CBFv. Of the 88 studies, 65 predominantly used traditional statistical time-series analysis, with transfer function analysis (TFA), wavelet analysis, and autoregressive (AR) models being prominent. Among machine learning algorithms, support vector machine (SVM) was widely utilized, and decision trees showed promise, especially in ICP prediction. Nonlinear models and multi-input models were prevalent, emphasizing the significance of multivariate modeling and forecasting. This review clarifies knowledge gaps and sets the stage for future research to advance cerebral physiologic signal analysis, benefiting neurocritical care applications.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Time-Series Modeling and Forecasting of Cerebral Pressure–Flow Physiology: A Scoping Systematic Review of the Human and Animal Literature

Vakitbilir,

Froese,

Gomez

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…There have been studies in the field of emergency medical service using Support Vector Machines (SVMs) (Nasiri et al 2009, Chiu et al 2010, Mehta et al 2007. All of which adapted SVMs on classification problems, but few study discuss the potential of the SVM's regression capabilities.…”

Section: Current Literaturementioning

confidence: 99%

A GIS-Based Demand Forecast Using Machine Learning for Emergency Medical Services

Chen

2014

Computing in Civil and Building Engineering (2014)

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The objective for pre-hospital Emergency Medical Service (EMS) is to reach to, pick up, and deliver patients efficiently. By increasing the operational efficiency, the survival rate of major trauma patients could potentially be improved. In this research, the authors applied Moving Average, Artificial Neural Network, Linear Regression, and Support Vector Machine for the forecast of pre-hospital emergency medical demand. The results from these approaches, as a reference, could be used for pre-allocation of ambulances. The training of the models as well as the validation is conducted with data collected from the New Taipei City EMS. In order to represent the performance of forecast, the authors introduced Geographic Information System (GIS) to manage and visualize the spatial distribution of forecast demand and error. Moreover, the authors built a flexible model, which organizes demand data with the user given size of area and size of time step. With the easy use of model and acceptable prediction performance, the research outcome has its potential to be applied to the current practice.

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Demand Forecast Using Data Analytics for the Preallocation of Ambulances

Chen

Huei‐Ming

et al. 2016

IEEE J. Biomed. Health Inform.

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The objective of prehospital emergency medical services (EMSs) is to have a short response time. By increasing the operational efficiency, the survival rate of patients could potentially be increased. The geographic information system (GIS) is introduced in this study to manage and visualize the spatial distribution of demand data and forecasting results. A flexible model is implemented in GIS, through which training data are prepared with user-desired sizes for the spatial grid and discretized temporal steps. We applied moving average, artificial neural network, sinusoidal regression, and support vector regression for the forecasting of prehospital emergency medical demand. The results from these approaches, as a reference, could be used for the preallocation of ambulances. A case study is conducted for the EMS in New Taipei City, where prehospital EMS data have been collected for three years. The model selection process has chosen different models with different input features for the forecast of different areas. The best daily mean absolute percentage error during testing of the EMS demand forecast is 23.01%, which is a reasonable forecast based on Lewis' definition. With the acceptable prediction performance, the proposed approach has its potential to be applied to the current practice.

show abstract

Classification of Diabetics with Various Degrees of Autonomic Neuropathy Based on Linear and Nonlinear Features Using Support Vector Machine

Cited by 3 publications

References 17 publications

Time-Series Modeling and Forecasting of Cerebral Pressure–Flow Physiology: A Scoping Systematic Review of the Human and Animal Literature

Time-Series Modeling and Forecasting of Cerebral Pressure–Flow Physiology: A Scoping Systematic Review of the Human and Animal Literature

A GIS-Based Demand Forecast Using Machine Learning for Emergency Medical Services

Demand Forecast Using Data Analytics for the Preallocation of Ambulances

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