gh-k-dense-ai-claude-scientific-skills-scientific-skills/skills/neurokit2/references/rsp.md

Respiratory Signal Processing

Overview

Respiratory signal processing in NeuroKit2 enables analysis of breathing patterns, respiratory rate, amplitude, and variability. Respiration is closely linked to cardiac activity (respiratory sinus arrhythmia), emotional state, and cognitive processes.

Main Processing Pipeline

rsp_process()

Automated processing of respiratory signals with peak/trough detection and feature extraction.

signals, info = nk.rsp_process(rsp_signal, sampling_rate=100, method='khodadad2018')

Pipeline steps:

1. Signal cleaning (noise removal, filtering)
2. Peak (exhalation) and trough (inhalation) detection
3. Respiratory rate calculation
4. Amplitude computation
5. Phase determination (inspiration/expiration)
6. Respiratory volume per time (RVT)

Returns:

• signals: DataFrame with:
  • RSP_Clean: Filtered respiratory signal
  • RSP_Peaks, RSP_Troughs: Extrema markers
  • RSP_Rate: Instantaneous breathing rate (breaths/min)
  • RSP_Amplitude: Breath-to-breath amplitude
  • RSP_Phase: Inspiration (0) vs. expiration (1)
  • RSP_Phase_Completion: Phase completion percentage (0-1)
  • RSP_RVT: Respiratory volume per time
• info: Dictionary with peak/trough indices

Methods:

• 'khodadad2018': Khodadad et al. algorithm (default, robust)
• 'biosppy': BioSPPy-based processing (alternative)

Preprocessing Functions

rsp_clean()

Remove noise and smooth respiratory signal.

cleaned_rsp = nk.rsp_clean(rsp_signal, sampling_rate=100, method='khodadad2018')

Methods:

1. Khodadad2018 (default):

• Butterworth low-pass filter
• Removes high-frequency noise
• Preserves breathing waveform

2. BioSPPy:

• Alternative filtering approach
• Similar performance to Khodadad

3. Hampel filter:

cleaned_rsp = nk.rsp_clean(rsp_signal, sampling_rate=100, method='hampel')

• Median-based outlier removal
• Robust to artifacts and spikes
• Preserves sharp transitions

Typical respiratory frequency:

• Adults at rest: 12-20 breaths/min (0.2-0.33 Hz)
• Children: faster rates
• During exercise: up to 40-60 breaths/min

rsp_peaks()

Identify inhalation troughs and exhalation peaks in respiratory signal.

peaks, info = nk.rsp_peaks(cleaned_rsp, sampling_rate=100, method='khodadad2018')

Detection methods:

• 'khodadad2018': Optimized for clean signals
• 'biosppy': Alternative approach
• 'scipy': Simple scipy-based detection

Returns:

• Dictionary with:
  • RSP_Peaks: Indices of exhalation peaks (maximum points)
  • RSP_Troughs: Indices of inhalation troughs (minimum points)

Respiratory cycle definition:

• Inhalation: Trough → Peak (air flows in, chest/abdomen expands)
• Exhalation: Peak → Trough (air flows out, chest/abdomen contracts)

rsp_findpeaks()

Low-level peak detection with multiple algorithm options.

peaks_dict = nk.rsp_findpeaks(cleaned_rsp, sampling_rate=100, method='scipy')

Methods:

• 'scipy': Scipy's find_peaks
• Custom threshold-based algorithms

Use case:

• Fine-tuned peak detection
• Custom parameter adjustment
• Algorithm comparison

rsp_fixpeaks()

Correct detected peak/trough anomalies (e.g., missed or false detections).

corrected_peaks = nk.rsp_fixpeaks(peaks, sampling_rate=100)

Corrections:

• Remove physiologically implausible intervals
• Interpolate missing peaks
• Remove artifact-related false peaks

Feature Extraction Functions

rsp_rate()

Compute instantaneous breathing rate (breaths per minute).

rate = nk.rsp_rate(peaks, sampling_rate=100, desired_length=None)

Method:

• Calculate inter-breath intervals from peak/trough timing
• Convert to breaths per minute (BPM)
• Interpolate to match signal length

Typical values:

• Resting adult: 12-20 BPM
• Slow breathing: <10 BPM (meditation, relaxation)
• Fast breathing: >25 BPM (exercise, anxiety)

rsp_amplitude()

Compute breath-to-breath amplitude (peak-to-trough difference).

amplitude = nk.rsp_amplitude(cleaned_rsp, peaks)

Interpretation:

• Larger amplitude: deeper breaths (tidal volume increase)
• Smaller amplitude: shallow breaths
• Variable amplitude: irregular breathing pattern

Clinical relevance:

• Reduced amplitude: restrictive lung disease, chest wall rigidity
• Increased amplitude: compensatory hyperventilation

rsp_phase()

Determine inspiration/expiration phases and completion percentage.

phase, completion = nk.rsp_phase(cleaned_rsp, peaks, sampling_rate=100)

Returns:

• RSP_Phase: Binary (0 = inspiration, 1 = expiration)
• RSP_Phase_Completion: Continuous 0-1 indicating phase progress

Use cases:

• Respiratory-gated stimulus presentation
• Phase-locked averaging
• Respiratory-cardiac coupling analysis

rsp_symmetry()

Analyze breath symmetry patterns (peak-trough balance, rise-decay timing).

symmetry = nk.rsp_symmetry(cleaned_rsp, peaks)

Metrics:

• Peak-trough symmetry: Are peaks and troughs equally spaced?
• Rise-decay symmetry: Is inhalation time equal to exhalation time?

Interpretation:

• Symmetric: normal, relaxed breathing
• Asymmetric: effortful breathing, airway obstruction

Advanced Analysis Functions

rsp_rrv()

Respiratory Rate Variability - analogous to heart rate variability.

rrv_indices = nk.rsp_rrv(peaks, sampling_rate=100)

Time-domain metrics:

• RRV_SDBB: Standard deviation of breath-to-breath intervals
• RRV_RMSSD: Root mean square of successive differences
• RRV_MeanBB: Mean breath-to-breath interval

Frequency-domain metrics:

• Power in frequency bands (if applicable)

Interpretation:

• Higher RRV: flexible, adaptive breathing control
• Lower RRV: rigid, constrained breathing
• Altered RRV: anxiety, respiratory disorders, autonomic dysfunction

Recording duration:

• Minimum: 2-3 minutes
• Optimal: 5-10 minutes for stable estimates

rsp_rvt()

Respiratory Volume per Time - fMRI confound regressor.

rvt = nk.rsp_rvt(cleaned_rsp, peaks, sampling_rate=100)

Calculation:

• Derivative of respiratory signal
• Captures rate of volume change
• Correlates with BOLD signal fluctuations

Use cases:

• fMRI artifact correction
• Neuroimaging preprocessing
• Respiratory confound regression

Reference:

• Birn, R. M., et al. (2008). Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI. NeuroImage, 31(4), 1536-1548.

rsp_rav()

Respiratory Amplitude Variability indices.

rav = nk.rsp_rav(amplitude, sampling_rate=100)

Metrics:

• Standard deviation of amplitudes
• Coefficient of variation
• Range of amplitudes

Interpretation:

• High RAV: irregular depth (sighing, arousal changes)
• Low RAV: stable, controlled breathing

Analysis Functions

rsp_analyze()

Automatically select event-related or interval-related analysis.

analysis = nk.rsp_analyze(signals, sampling_rate=100)

Mode selection:

• Duration < 10 seconds → event-related
• Duration ≥ 10 seconds → interval-related

rsp_eventrelated()

Analyze respiratory responses to specific events/stimuli.

results = nk.rsp_eventrelated(epochs)

Computed metrics (per epoch):

• RSP_Rate_Mean: Average breathing rate during epoch
• RSP_Rate_Min/Max: Minimum/maximum rate
• RSP_Amplitude_Mean: Average breath depth
• RSP_Phase: Respiratory phase at event onset
• Dynamics of rate and amplitude across epoch

Use cases:

• Respiratory changes during emotional stimuli
• Anticipatory breathing before task events
• Breath-holding or hyperventilation paradigms

rsp_intervalrelated()

Analyze extended respiratory recordings.

results = nk.rsp_intervalrelated(signals, sampling_rate=100)

Computed metrics:

• RSP_Rate_Mean: Average breathing rate
• RSP_Rate_SD: Variability in rate
• RSP_Amplitude_Mean: Average breath depth
• RRV indices (if sufficient data)
• RAV indices

Recording duration:

• Minimum: 60 seconds
• Optimal: 5-10 minutes

Use cases:

• Resting state breathing patterns
• Baseline respiratory assessment
• Stress or relaxation monitoring

Simulation and Visualization

rsp_simulate()

Generate synthetic respiratory signals for testing.

synthetic_rsp = nk.rsp_simulate(duration=60, sampling_rate=100, respiratory_rate=15,
                                method='sinusoidal', noise=0.1, random_state=42)

Methods:

• 'sinusoidal': Simple sinusoidal oscillation (fast)
• 'breathmetrics': Advanced realistic breathing model (slower, more accurate)

Parameters:

• respiratory_rate: Breaths per minute (default: 15)
• noise: Gaussian noise level
• random_state: Seed for reproducibility

Use cases:

• Algorithm validation
• Parameter tuning
• Educational demonstrations

rsp_plot()

Visualize processed respiratory signal.

nk.rsp_plot(signals, info, static=True)

Displays:

• Raw and cleaned respiratory signal
• Detected peaks and troughs
• Instantaneous breathing rate
• Phase markers

Interactive mode: Set static=False for Plotly visualization

Practical Considerations

Sampling Rate Recommendations

• Minimum: 10 Hz (adequate for rate estimation)
• Standard: 50-100 Hz (research-grade)
• High-resolution: 1000 Hz (typically unnecessary, oversampled)

Recording Duration

• Rate estimation: ≥10 seconds (few breaths)
• RRV analysis: ≥2-3 minutes
• Resting state: 5-10 minutes
• Circadian patterns: Hours to days

Signal Acquisition Methods

Strain gauge/piezoelectric belt:

• Chest or abdominal expansion
• Most common
• Comfortable, non-invasive

Thermistor/thermocouple:

• Nasal/oral airflow temperature
• Direct airflow measurement
• Can be intrusive

Capnography:

• End-tidal CO₂ measurement
• Gold standard for physiology
• Expensive, clinical settings

Impedance pneumography:

• Derived from ECG electrodes
• Convenient for multi-modal recording
• Less accurate than dedicated sensors

Common Issues and Solutions

Irregular breathing:

• Normal in awake, resting humans
• Sighs, yawns, speech, swallowing cause variability
• Exclude artifacts or model as events

Shallow breathing:

• Low signal amplitude
• Check sensor placement and tightness
• Increase gain if available

Movement artifacts:

• Spikes or discontinuities
• Minimize participant movement
• Use robust peak detection (Hampel filter)

Talking/coughing:

• Disrupts natural breathing pattern
• Annotate and exclude from analysis
• Or model as separate event types

Best Practices

Standard workflow:

# 1. Clean signal
cleaned = nk.rsp_clean(rsp_raw, sampling_rate=100, method='khodadad2018')

# 2. Detect peaks/troughs
peaks, info = nk.rsp_peaks(cleaned, sampling_rate=100)

# 3. Extract features
rate = nk.rsp_rate(peaks, sampling_rate=100, desired_length=len(cleaned))
amplitude = nk.rsp_amplitude(cleaned, peaks)
phase = nk.rsp_phase(cleaned, peaks, sampling_rate=100)

# 4. Comprehensive processing (alternative)
signals, info = nk.rsp_process(rsp_raw, sampling_rate=100)

# 5. Analyze
analysis = nk.rsp_analyze(signals, sampling_rate=100)

Respiratory-cardiac integration:

# Process both signals
ecg_signals, ecg_info = nk.ecg_process(ecg, sampling_rate=1000)
rsp_signals, rsp_info = nk.rsp_process(rsp, sampling_rate=100)

# Respiratory sinus arrhythmia (RSA)
rsa = nk.hrv_rsa(ecg_info['ECG_R_Peaks'], rsp_signals['RSP_Clean'], sampling_rate=1000)

# Or use bio_process for multi-signal integration
bio_signals, bio_info = nk.bio_process(ecg=ecg, rsp=rsp, sampling_rate=1000)

Clinical and Research Applications

Psychophysiology:

• Emotion and arousal (rapid, shallow breathing during stress)
• Relaxation interventions (slow, deep breathing)
• Respiratory biofeedback

Anxiety and panic disorders:

• Hyperventilation during panic attacks
• Altered breathing patterns
• Breathing retraining therapy effectiveness

Sleep medicine:

• Sleep apnea detection
• Breathing pattern abnormalities
• Sleep stage correlates

Cardiorespiratory coupling:

• Respiratory sinus arrhythmia (HRV modulation by breathing)
• Heart-lung interaction
• Autonomic nervous system assessment

Neuroimaging:

• fMRI artifact correction (RVT regressor)
• BOLD signal confound removal
• Respiratory-related brain activity

Meditation and mindfulness:

• Breath awareness training
• Slow breathing practices (resonance frequency ~6 breaths/min)
• Physiological markers of relaxation

Athletic performance:

• Breathing efficiency
• Training adaptations
• Recovery monitoring

Interpretation Guidelines

Breathing rate:

• Normal: 12-20 BPM (adults at rest)
• Slow: <10 BPM (relaxation, meditation, sleep)
• Fast: >25 BPM (exercise, anxiety, pain, fever)

Breathing amplitude:

• Tidal volume typically 400-600 mL at rest
• Deep breathing: 2-3 L
• Shallow breathing: <300 mL

Respiratory patterns:

• Normal: Smooth, regular sinusoidal
• Cheyne-Stokes: Crescendo-decrescendo with apneas (clinical pathology)
• Ataxic: Completely irregular (brainstem lesion)

References

• Khodadad, D., Nordebo, S., Müller, B., Waldmann, A., Yerworth, R., Becher, T., ... & Bayford, R. (2018). A review of tissue substitutes for ultrasound imaging. Ultrasound in medicine & biology, 44(9), 1807-1823.
• Grossman, P., & Taylor, E. W. (2007). Toward understanding respiratory sinus arrhythmia: Relations to cardiac vagal tone, evolution and biobehavioral functions. Biological psychology, 74(2), 263-285.
• Birn, R. M., Diamond, J. B., Smith, M. A., & Bandettini, P. A. (2006). Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI. NeuroImage, 31(4), 1536-1548.