TFM-Tokenizer learns a vocabulary of time-frequency motifs from single-channel EEG via a dual-path masked architecture and encodes signals into discrete tokens, reporting up to 11% Cohen's Kappa gains on benchmarks and 14% on ear-EEG sleep staging.
Motiflets–simple and accurate detection of motifs in time series
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A motif-based framework extracts representative ECG beats with DTW and defines deviation-from-NSR, deviation-from-baseline, and instability metrics that statistically separate normal from abnormal recordings on PTB-XL and MIT-BIH.
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Tokenizing Single-Channel EEG with Time-Frequency Motif Learning
TFM-Tokenizer learns a vocabulary of time-frequency motifs from single-channel EEG via a dual-path masked architecture and encodes signals into discrete tokens, reporting up to 11% Cohen's Kappa gains on benchmarks and 14% on ear-EEG sleep staging.
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Motif-based morphology signatures for interpretable ECG screening and monitoring
A motif-based framework extracts representative ECG beats with DTW and defines deviation-from-NSR, deviation-from-baseline, and instability metrics that statistically separate normal from abnormal recordings on PTB-XL and MIT-BIH.