Table 3.
Baseline characteristics of studies detecting adverse BGa events (N=17).
| First author (year), country | Data source | Sample size | Object; setting | Model | Time | Age (years), mean (SD)/range | Threshold | |||||
| Patients, n | Data points, n | Hypoglycemia, n | ||||||||||
| Jin (2019), United States [10] | EHRsb | —c | 4104 | 132 | T1DMd; in | Linear discriminant analysis (LDA) | All | — | — | |||
| Nguyen (2013), Australia [16] | EEGe | 5 | 144 | 76 | T1DM; in | Levenberg-Marquardt (LM), genetic algorithm (GA) | All | 12-18 | 3.3 | |||
| Chan (2011), Australia [40] | CGMf device | 16 | 100 | 52 | T1DM; experimental | Feed-forward neural network (fNN) | Nocturnal | 14.6 (SD 1.5) | 3.3 | |||
| Nguyen (2010), Australia [41] | EEG | 6 | 79 | 27 | T1DM; experimental | Block-based neural network (BRNN) | Nocturnal | 12-18 | 3.3 | |||
| Rubega (2020), Italy [42] | EEG | 34 | 2516 | 1258 | T1DM; experimental | NNMg | All | 55 (SD 3) | 3.9 | |||
| Chen (2019), United States [43] | EEG | — | 300 | 11 | DMh; in | Logistic regression (LR) | All | — | — | |||
| Jensen (2013), Denmark [44] | CGM device | 10 | 1267 | 160 | T1DM; experimental | SVMi | All | 44 (SD 15) | 3.9 | |||
| Skladnev (2010), Australia [45] | CGM device | 52 | 52 | 11 | T1DM; in | fNN | Nocturnal | 16.1 (SD 2.1) | 3.9 | |||
| Iaione (2005), Brazil [46] | EEG | 8 | 1990 | 995 | T1DM; experimental | NNM | Morning | 35 (SD 13.5) | 3.3 | |||
| Nuryani (2012), Australia [61] | ECG | 5 | 575 | 133 | DM; in | SVM, linear multiple regression (LMR) | All | 16 (SD 0.7) | 3.0 | |||
| San (2013), Australia [62] | ECG | 15 | 440 | 39 | T1DM; in | Block-based neural network (BBNN), wavelet neural network (WNN), fNN, SVM | All | 14.6 (SD 1.5) | 3.3 | |||
| Ling (2012), Australia [63] | ECG | 16 | 269 | 54 | T1DM; in | Fuzzy reasoning model (FRM), fNN, multiple regression–fuzzy inference system (MR-FIS) | Nocturnal | 14.6 (SD 1.5) | 3.3 | |||
| Ling (2016), Australia [64] | ECG | 16 | 269 | 54 | T1DM; in | Extreme learning machine–based neural network (ELM-NN), particle swarm optimization–based neural network (PSO-NN), MR-FIS, LMR, fuzzy inference system (FIS) | Nocturnal | 14.6 (SD 1.5) | 3.3 | |||
| Nguyen (2012), Australia [65] | EEG | 5 | 44 | 20 | T1DM; in | NNM | — | 12-18 | 3.3 | |||
| Ngo (2020), Australia [66] | EEG | 8 | 135 | 53 | T1DM; in | BRNN | Nocturnal | 12-18 | 3.9 | |||
| Ngo (2018), Australia [67] | EEG | 8 | 54 | 26 | T1DM; in | BRNN | Nocturnal | 12-18 | 3.9 | |||
| Nuryani (2010), Australia [68] | ECG | 5 | 27 | 8 | T1DM; experimental | Fuzzy support vector machine (FSVM), SVM | Nocturnal | 16 (SD 0.7) | 3.3 | |||
aBG: blood glucose.
bEHR: electronic health record.
cNot applicable.
dT1DM: type 1 diabetes mellitus.
eEEG: electroencephalograph.
fCGM: continuous glucose monitoring.
gNNM: neural network model.
hDM: diabetes mellitus.
iSVM: support vector machine.