GPU-Accelerated Benchmarking of Transformer and Classical Models for Fetal Monitoring Time Series (CTG)

Dataset Description This dataset accompanies a comprehensive GPU-based benchmarking study of classical and transformer-based time-series models for fetal cardiotocography (CTG) signal analysis. The dataset was derived from clinical recordings exported from the MFM-CNS fetal monitoring system and processed to enable fair model comparison across deep learning and transformer architectures. The dataset is structured as follows: 01_ExamData.csv – The original raw data exported from the hospital information system. Each record may contain multiple patient sessions, as collected directly from the obstetric monitoring system. 02_SplitData.ipynb – The preprocessing notebook used to segment the raw data into individual patient-level sequences. This notebook performs filtering, cleaning, and restructuring of the input file into uniform arrays. _scored_output.xlsx – The finalized and de-identified dataset used in all machine learning (ML) and deep learning (DL) analyses. Each row represents one distinct patient recording with synchronized FHR (fetal heart rate), UA (uterine activity), and AFM (automatic fetal movement) channels, together with expert clinical scores. Additional files include model-level outputs, evaluation metrics, and scripts for full reproducibility: ctg_gpu_benchmark_v3_full.py – The main Python script containing all model definitions, GPU training routines, and metric computations. 19_GPU.ipynb – The Colab-ready notebook for running the full benchmarking pipeline. metrics_summary.csv, threshold_metrics.csv, model_complexity.csv, stability_mae.csv, wilcoxon_BH.csv, predictions_test.csv – Summary tables reporting accuracy, mean absolute error (MAE), model complexity, stability tests, and statistical comparisons. TRIPOD_report.txt – Transparent reporting checklist for model development and validation consistency. How to Reproduce Launch a Google Colab environment with GPU runtime enabled. Upload ctg_gpu_benchmark_v3_full.py and _scored_output.xlsx into the working directory. Open and execute 19_GPU.ipynb sequentially to reproduce all experiments, figures, and metric tables. This setup ensures full reproducibility of the model comparison results under identical preprocessing and data splits. Benchmarked Models Ten supervised models were trained and evaluated using identical data splits and hyperparameter budgets: Classical Deep Time-Series Models ResNet1D – Residual 1D convolutional architecture for hierarchical temporal abstraction. InceptionTime – Multi-scale inference via parallel convolutions with varying kernel sizes. TCN (Temporal Convolutional Network) – Dilated causal convolutions for long-range temporal dependency learning. LSTM-FCN – Hybrid model combining convolutional feature extraction with bidirectional LSTM layers. TimesNet-Lite – Frequency-domain representation of temporal patches for efficient learning. Transformer and Mixer-Based Models Vanilla Transformer – Multi-head self-attention with sinusoidal positional encodings. TSMixer – Alternating temporal- and feature-mixing operations for parameter-efficient representation learning. PatchTST – Patch-wise segmentation of sequences to capture long-term dependencies with transformer attention. iTransformer – Channel-transposed attention mechanism directly modeling inter-variable relationships. TST (Time Series Transformer) – Transformer variant optimized for regression tasks on multivariate physiological signals. Intended Use This dataset provides a reproducible foundation for fetal monitoring research, benchmarking new architectures in physiological time-series analysis, and validating transformer-based temporal models in clinical settings. All data are anonymized and derived from ethically approved retrospective sources.数据集说明 本数据集配套一项基于图形处理器（GPU, Graphics Processing Unit）的全面基准测试研究，该研究针对用于胎儿胎心监护（CTG, Cardiotocography）信号分析的经典与时序Transformer模型展开全面对比。数据集源自MFM-CNS胎儿监护系统导出的临床记录，经过标准化处理以确保深度学习与Transformer架构间的模型对比公平性。 数据集组织结构如下： 01_ExamData.csv —— 从医院信息系统导出的原始未处理数据。每条记录可包含多个患者会话数据，直接取自产科监护系统。 02_SplitData.ipynb —— 用于将原始数据分割为独立患者级序列的Jupyter预处理脚本。该脚本完成输入文件的滤波、清洗与重构，将其转换为统一格式的数组。 _scored_output.xlsx —— 用于所有机器学习（ML, Machine Learning）与深度学习（DL, Deep Learning）分析的最终去标识化数据集。每一行对应一条独立的患者监护记录，包含同步的胎儿心率（FHR, Fetal Heart Rate）、子宫收缩活动（UA, Uterine Activity）与自动胎儿运动（AFM, Automatic Fetal Movement）通道数据，同时附带专家临床评分。 附加文件包含模型级输出、评估指标与完整复现所需的脚本： ctg_gpu_benchmark_v3_full.py —— 主Python脚本，包含所有模型定义、GPU训练流程与指标计算逻辑。 19_GPU.ipynb —— 适配Google Colab运行的完整基准测试流水线Jupyter脚本。 metrics_summary.csv、threshold_metrics.csv、model_complexity.csv、stability_mae.csv、wilcoxon_BH.csv、predictions_test.csv —— 汇总表格，分别报告模型准确率、平均绝对误差（MAE, Mean Absolute Error）、模型复杂度、稳定性测试结果与统计对比分析结果。 TRIPOD_report.txt —— 用于模型开发与验证一致性的透明报告核查清单。 复现方法 1. 启动启用GPU运行时的Google Colab环境。 2. 将ctg_gpu_benchmark_v3_full.py与_scored_output.xlsx上传至工作目录。 3. 依次打开并执行19_GPU.ipynb，即可复现所有实验、图表与指标表格。 该配置可确保在相同预处理与数据划分流程下，完整复现模型对比结果。 基准测试模型 本研究使用完全一致的数据划分与超参数配置预算，训练并评估了十款监督学习模型： 经典深度时序模型 ResNet1D —— 用于层级化时间抽象的一维残差卷积架构。 InceptionTime —— 通过不同卷积核尺寸的并行卷积实现多尺度推理的模型。 TCN（时序卷积网络, Temporal Convolutional Network） —— 使用扩张因果卷积学习长程时序依赖关系的模型。 LSTM-FCN —— 结合卷积特征提取与双向长短期记忆（LSTM, Long Short-Term Memory）层的混合模型。 TimesNet-Lite —— 对时序补丁进行频域表征以实现高效学习的模型。 Transformer与混合器类模型 Vanilla Transformer —— 采用正弦位置编码的多头自注意力模型。 TSMixer —— 通过交替时序与特征混合操作实现参数高效表征学习的模型。 PatchTST —— 对序列进行分块处理，借助Transformer注意力捕获长程依赖的模型。 iTransformer —— 直接建模变量间关联关系的通道转置注意力机制模型。 TST（时序Transformer, Time Series Transformer） —— 针对多变量生理信号回归任务优化的Transformer变体模型。 预期用途 本数据集为胎儿监护研究提供可复现的基础框架，可用于生理时序分析领域新架构的基准测试，以及临床场景下基于Transformer的时序模型验证。所有数据均已匿名化处理，源自经伦理审批的回顾性研究来源。