Nonlinear Metrics

How it works

Comparisons

Highlighted claims

• The Lyapunov-like instability proxy was intended to capture local fluctuation magnitude and was not treated as a formal largest Lyapunov exponent. — Recurrence-Based Nonlinear Vocal Dynamics as Digital Biomarkers for Depression Detection from Conversational Speech
• The study compared recurrence biomarkers with static acoustic summaries, entropy biomarkers, forecastability features, Hurst exponent features, a Lyapunov-like instability proxy, and a determinism proxy. — Recurrence-Based Nonlinear Vocal Dynamics as Digital Biomarkers for Depression Detection from Conversational Speech
• The Hurst exponent performed poorly, indicating that long-memory scaling alone did not characterize depression-related speech changes in the dataset. — Recurrence-Based Nonlinear Vocal Dynamics as Digital Biomarkers for Depression Detection from Conversational Speech
• The determinism proxy was weak, suggesting diagonal recurrence structure was not the primary signal. — Recurrence-Based Nonlinear Vocal Dynamics as Digital Biomarkers for Depression Detection from Conversational Speech
• Combining Lyapunov-like features with recurrence features reportedly did not improve performance beyond recurrence alone. — Recurrence-Based Nonlinear Vocal Dynamics as Digital Biomarkers for Depression Detection from Conversational Speech