kempt-kinkajou-2023/weather_platform/apps/py-tg-bot-weather-agent/examples/files/extracted_data.csv

1	Rank	Paper Title	Score	Data preprocessing	Data augmentation	Imbalance	End-to-end	Ensemble	Multi-binary classification	Feature engineering	Hand Features	Temporal	Frequent	Demographic Features	DNN	CNN	RNN/Transformer	Attention	Backbone	Others
2	1	A Wide & Deep Transformer Neural Network for 12-Lead ECG Classification	0.533	Sampling rate 500Hz; Bandwidth 3 - 45 Hz; Value -1 - 1; Window size 15s 0-padding	No	No	Yes	Top 3 best models from 10 folds CV	Yes	Yes	No	No	No	age,sex + 20 top identified features by random forest model	Yes	Yes	Transformer	Inherently in Transformer (location wise)	ResNet + Transformer + hand features + ensemble	None
3	2	Adaptive lead weighted ResNet trained with different duration signals for classifying 12-lead ECGs	0.52	Sampling rate 257 Hz; Value -1 - 1; Window size 4096(16s) 0-padding	No	Thresholding	Yes	Ensemble 5 CV models	Yes	Yes	No	No	No	only age and sex	Yes	Yes	No	Squeeze-And-Excitation (channel wise)	ResNet + SE	None
4	3	Classification of Cardiac Abnormalities from ECG Signals using SE-ResNet	0.514	Sampling rate 500Hz; Window size 30s 0-padding; Exclude 4 leads; Wavelet denoise	With external data: Hefei	Sign loss	Yes	Rule model for bradycardia	Yes	No	No	No	No	No	Yes	Yes	No	Squeeze-And-Excitation (channel wise)	ResNet + SE	None
5	4	Combining Scatter Transform and Deep Neural Networks for Multilabel Electrocardiogram Signal Classification	0.485	Sampling rate 500Hz; Value -1 - 1; Window size 10240(20.48 s)	Add noise and drift	No	Yes	No	Yes	No	No	No	No	No	Yes	Yes	Transformer	Inherently in Transformer (location wise)	ResNet + Transformer	None
6	5	Classification of 12-lead ECG Signals with Adversarial Multi-Source Domain Generalization	0.437	Sampling rate 250Hz; Value normalized	Add or filter frequency components; Substitute, shuffle, invert, filt and scale lead data	No	Yes	No	Yes	Yes	No	No	No	only age and sex	Yes	Yes	Yes	No	ResNet + RNN	Domain-adversarial training
7	6	Bag of Tricks for Electrocardiogram Classification with Deep Neural Networks	0.42	Sampling rate 500Hz; Bandwith 50-60Hz; Value use mean and std; Exlude PTB and StPetersburg datasets	Manifold Mixup	Weighted loss, Thresholding	Yes	Ensemble 10 CV models	Yes	No	No	No	No	No	Yes	Yes	No	No	ResNet	None
8	7	Automated Comprehensive Interpretation of 12-lead Electrocardiograms Using Pre-trained Exponentially Dilated Causal Convolutional Neural Networks	0.417	Sampling rate 500Hz	With external data: UMCU	Weighted loss (focal)	Yes	No	Yes	No	No	No	No	No	Yes	Yes	No	No	ResNet	None
9	8	SE-ECGNet : Multi-scale SE-Net for Multi-lead ECG Data	0.411	Window sizw 10s	Random shift signals; Add Gaussian noise	Thresholding	Yes	No	Yes	Yes	No	No	No	only age and sex	Yes	Yes	No	Squeeze-And-Excitation (channel wise)	ResNet + SE	None
10	9	Impact of Neural Architecture Design on Cardiac Abnormality Classification Using 12-lead ECG Signals	0.382	Window size 10s 0-padding	No	Thresholding	Yes	No	Yes	No	No	No	No	No	Yes	Yes	Yes	No	ResNet + BiLSTM	NAS
11	10	Automatic Detection and Classification of 12-lead ECGs Using a Deep Neural Network	0.359	Sampling rate 500Hz; Window size 10s 0-padding	No	No	Yes	No	Yes	Yes	No	No	No	only age and sex	Yes	Yes	No	Squeeze-And-Excitation (channel wise)	ResNet + SE	None
12	11	Cardiac Pathologies Detection and Classification in 12-lead ECG	0.354	No	No	No	No	No	Yes	Yes	Yes	Yes	Yes	No	No	No	No	No	Rule based	None
13	12	Rule-Based Method and Deep Learning Networks for Automatic Classification of ECG	0.298	Sampling rate 500 Hz	No	At most N_max instances	No	No	Yes	Yes	Yes	Yes	Yes	No	Yes	Yes	No	No	Rule	CWT transforms signal to scalogram image
14	13	Convolutional Recurrent Neural Network and LightGBM Ensemble Model for 12-lead ECG Classification	0.281	Samping rate 500Hz; Bandwidth 0.01 - 20 Hz; Window size 10s	No	SMOTE	No	Yes	Yes	Yes	Yes	Yes	Yes	only age and sex	Yes	Yes	Yes	Yes	CRNN + LightGBM	None
15	14	12-lead ECG Arrythmia Classification Using Convolutional Neural Network for Mutually Non-Exclusive Classes	0.279	Samping rate 100Hz, Window size 10 s; Median filter with 5 samples; Subtract mean of 50 samples	No	No	Yes	No	Yes	No	No	No	No	No	Yes	Yes	No	No	CNN	None
16	15	Cardiac Abnormality Detection in 12-lead ECGs With Deep Convolutional Neural Networks Using Data Augmentation	0.27	Sampling rate 500 Hz; Bandwidth 0.5 - 45 Hz; Uniform filter with 50 samples	Signal slicing; CutOut; Channel shifting; Additive noise; Dropout	No	Yes	No	No	No	No	No	NO	NO	Yes	Yes	No	No	ResNet	Data augmentation
17	16	A Bio-toolkit for Multi-Cardiac Abnormality Diagnosis Using ECG Signal and Deep Learning	0.26	Cardiac cycle 400 samples; Bandwidth 3 - 45 Hz;	No	No	Yes	No	No	No	No	No	No	No	Yes	Yes	LSTM	No	CNN+LSTM	None
18	17	Multi-label Arrhythmia Classification From 12-Lead Electrocardiograms	0.24	Bandwidth 12 - 50 Hz; Median filter with 10ms;	No	Undersample the majority class	No (Feature extraction)	No	Yes	Yes	Yes	Yes	Yes	No	Yes	Yes	Yes	No	AlexNet, LSTM, SVM… for different classes	Feature Map Generation algorithm design
19	18	MADNN : A Multi-scale Attention Deep Neural Network for Arrhythmia Classification Data preprocessing	0.24	Sampling rate 500 Hz, Window size 30000(60 s) copy-padding	Romdomly crop	Focal loss; Balance factor to filter samples	Yes	VGG, Xception	No	No	No	No	No	No	Yes	Yes	No	Yes	ResNet+SE+SK	Data augmentation
20	19	Classification of 12-lead ECGs Using Gradient Boosting on Features Acquired With Domain-Specific and Domain-Agnostic Methods	0.23	Bandwidth 0-80 Hz; Median filter with 200 ms and 600 ms	No	Re-weight positive samples	No (Feature extraction)	No	No	Yes	Yes	Yes	Yes	No	No	No	No	No	XGBoost	Feature extraction
21	20	Convolutional Neural Network and Rule-Based Algorithms for Classifying 12-lead ECGs	0.21	Samping rate 500Hz; Window size 5000(10s) 0-padding	No	No	Yes	No	No	Yes	No	No	No	only age and sex	Yes	Yes	No	No	CNN+Rule-based model (HRV-score)	RMSSD
22	21	Automated Classification of Electrocardiograms Using Wavelet Analysis and Deep Learning	0.21	No	No	Undersample the majority class	No	No	Yes	Yes	Yes	No	Yes	No	Yes	Yes	No	No	SqueezeNet, 12 models for each lead with voting scheme	None
23	22	ECG Abnormalities Recognition Using Convolutional Network With Global Skip Connections and Custom Loss Function	0.2	Sampling rate 125 Hz	No	WCE loss function	Yes	No	No	No	No	No	No	No	Yes	Yes	No	No	ResNet+Custom Loss Function	None
24	23	Multi-Class Classification of Pathologies Found on Short ECG Signals	0.19	Samping rate250Hz	No	No	Yes	No	Yes	Yes	Yes	Yes	No	No	No	No	No	No	ECOC, Bagging	None
25	24	ECG Classification with a Convolutional Recurrent Neural Network	0.17	Samping rate 257 Hz, Bandwidth 0.5- Hz	Random offset	Focal loss; Oversampling; MLSMOTE; An ensemble of binary and multi-label deep models	Yes	No	No	No	No	No	No	No	Yes	Yes	GRU	No	CRNN	Time Augmentation
26	25	Interpretable XGBoost Based Classification of 12-lead ECGs Applying Information Theory Measures From Neuroscience	0.16	Bandwidth 1.5-25 Hz; db4	No	Undersample the majority class	No (Feature extraction)	No	Yes	Yes	Yes	Yes	Yes	known patient metadata.	No	No	No	No	XGBoost	None
27	26	Identification of Cardiac Arrhythmias from 12-lead ECG using Beat-wise Analysis and a Combination of CNN and LSTM	0.15	Sampling rate 500 Hz; Window size 10s 0-padding, Bandwith 0-50 Hz; MODWT; db5; LOESS	No	Select most significant beats number	Yes	No	No	No	No	No	No	No	Yes	Yes	LSTM	No	CNN+BiLSTM	None
28	27	Classification of 12-Lead Electrocardiograms Using Residual Neural Networks and Transfer Learning	0.14	Sampling rate 250Hz; Window size 10s	With external data: MUSE	No	Yes	No	No	No	No	No	No	No	Yes	Yes	No	No	ResNet	None
29	28	ECG Segmentation Using a Neural Network as the Basis for Detection of Cardiac Pathologies	0.14	Sampling rate 500 Hz; Bandwith 0.05-42 Hz; db4	No	Focal loss	No (Feature extraction)	No	Yes	Yes	Yes	Yes	No	No	Yes	Yes	No	No	Unet+XGBoost	None
30	29	Automatic 12-lead ECG Classification Using a Convolutional Network Ensemble	0.132	Sampling rate 400hz; Window size 4096 0-padding	No	Correlation factor	Yes	7 convolutional models	No	No	No	No	No	No	Yes	Yes	No	No	ResNet	None
31	30	Utilization of Residual CNN-GRU With Attention Mechanism for Classification of 12-lead ECG	0.122	Sampling rate 250 Hz	Power envelopes	No	Yes	Yes	No	No	No	No	No	No	Yes	Yes	GRU	No	ResNet + RNN	None
32	31	Selected Features for Classification of 12-lead ECGs	0.102	Bandwidth 1- Hz; Median filter	No	No	No	Bootstrap-aggregated	No	Yes	Yes	Yes	No	only age and sex	No	No	No	No	Decision tree	Featre extraction
33	32	Classification of 12 Lead ECG Signal Using 1D-Convolutional Neural Network With Class Dependent Threshold	0.077	Sampling rate 500 Hz; Window size 10000 copy-padding; Converted to physical unit(mV)	No	No	Yes	No	Yes	No	No	No	No	No	Yes	Yes	No	No	CNN	None
34	33	Deep Multi-Label Multi-Instance Classification on 12-Lead ECG	0.001	Bandwidth 0.5 - 50 Hz	Randomly sampled	BCELoss	Yes	No	Yes	No	No	No	No	No	Yes	Yes	No	Yes	ResNet Encoder+attention-based MIC+Decoder	None
35	34	Diagnostic of Multiple Cardiac Disorders from 12-lead ECGs Using Graph Convolutional Network Based Multi-label Classification	-0.012	Sampling rate 500 Hz; Window size 18s 0-padding	No	No	Yes	Yes	Yes	No	No	No	No	No	Yes	Yes	GRU	No	CNN+BiGRU+GCN	CNN+BiGRU+GCN(GRN for label correlations)
36	35	Automatic Concurrent Arrhythmia Classification Using Deep Residual Neural Networks	-0.035	Window size 2500	No	No	Yes	Yes	No	No	No	No	No	No	Yes	Yes	RNN GRU LSTM	Yes	ResNet	None
37	36	Multilabel 12-Lead Electrocardiogram Classification Using Gradient Boosting Tree Ensemble	-0.08	Sampling rate 0-500 Hz; Window middle 2,000 samples(R:-0.35-0.5s); Average filter with 0.02s;	No	Oversampling; Re-weight	No	Yes	Yes	Yes	Yes	Yes	No	No	No	No	No	No	XGboost	None
38	37	A Topology Informed Random Forest Classifier for ECG Classification	-0.113	1800 time points	No	No	No	No	No	Yes	Yes	Yes	No	demographic data	No	No	No	No	random forest	point cloud embedding
39	38	Detection of Cardiac Arrhythmias From Varied Length Multichannel Electrocardiogram Recordings Using Deep Convolutional Neural Networks	-0.128	Window size 3000 0-padding	No	Thresholding	Yes	No	No	No	No	No	No	No	Yes	Yes	No	No	VGG16	None
40	39	Multi-Stream Deep Neural Network For 12-Lead ECG Classification	-0.29	Bandwidth 0.1-30 Hz	No	No	No (Feature extraction)	Yes	No	Yes	Yes	Yes	No	No	Yes	Yes	No	No	CNN	CNN+feature extraction
41	40	Classification of 12-lead ECG with an Ensemble Machine Learning Approach	-0.476	Sampling rate 500 Hz; Bandwidth 0.67-30 Hz	Vector magnitude for detect beats	No	No (Feature extraction)	Yes	Yes	Yes	Yes	Yes	No	No	Yes	Yes	No	No	CNN	3 CNNs for signal + FFNN for phythm + PVC detection
42	41	Classification of 12-lead ECGs using digital biomarkersand representation learning	-0.658	Sampling rate 500 Hz; Bandwidth 0.13-88 Hz; bSQ>0.8;	No	Oversampling	No	Yes	No	Yes	Yes	Yes	No	only age and sex	Yes	Yes	GRU	No	CNN+GRU	feature engineering + CNN