❤️⚡ CARDIO-RS ⚡❤️

A Rust library for Heart Rate Variability (HRV) analysis

📊 Compute HRV metrics in time-domain, frequency-domain, and non-linear domain from RR intervals with ease!
💓 Preprocess ECG & PPG raw data for accurate HRV analysis!
📟 Compatible with embedded systems using no_std!

✨ Key Features

• ✅ Time-domain HRV metrics: SDNN, RMSSD, PNN50, SDSD, CVSD, and more!
• ✅ Frequency-domain HRV metrics: LF, HF, VLF (using Welch’s method).
• ✅ Geometric HRV metrics: Triangular Index & TINN.
• ✅ Non-linear HRV metrics: SampEn, DFA, LZC, and more!
• ✅ ECG & PPG preprocessing: Filtering, denoising, peak detection.
• ✅ no_std compatibility for embedded systems.
• ✅ Window-based analysis: Split data into windows for segment-based HRV calculations.
• ✅ Live HRV analysis: Real-time processing of RR intervals with customizable pipelines.

📏 Time-domain HRV Metrics

• 🔹 RMSSD – Short-term HRV measurement.
• 🔹 SDNN – Overall HRV measurement.
• 🔹 PNN50 – Percentage of successive RR intervals > 50ms.
• 🔹 SDSD – Standard deviation of successive RR intervals.
• 🔹 Mean HR – Average heart rate.
• 🔹 AVNN – Mean of all RR intervals.
• 🔹 CVSD – RMSSD divided by the mean RR interval.

📐 Geometric HRV Metrics

• 📊 Triangular Index – Measures RR interval distribution.
• 📊 TINN (Triangular Interpolation of NN Interval Histogram) – Estimates RR variability.

🎵 Frequency-domain HRV Metrics

Computed using Welch’s method for spectral density estimation:

• 🎼 LF (Low Frequency) – Sympathetic and parasympathetic balance (0.04 - 0.15 Hz).
• 🎼 HF (High Frequency) – Parasympathetic activity (0.15 - 0.40 Hz).
• 🎼 VLF (Very Low Frequency) – Long-term regulatory processes (0.003 - 0.04 Hz).

🧩 Non-linear HRV Metrics

Non-linear HRV metrics provide insights into the complexity and self-organization of the heart's autonomic regulation. They are useful for capturing dynamics that cannot be fully understood through linear analysis alone.

• 🔹 SampEn (Sample Entropy) – Measures the regularity and unpredictability of a time series.
• 🔹 DFA (Detrended Fluctuation Analysis) – Evaluates long-range correlations and scaling behavior in the RR intervals.
• 🔹 LZC (Lempel-Ziv Complexity) – Quantifies the complexity of a binary sequence derived from RR intervals.

🏥 ECG & PPG Data Processing

• 📡 ECG Processing: R-wave peak detection, filtering, and RR extraction.
• 💡 PPG Processing: Pulse detection & signal cleaning for HRV computation.

🪟 Window Analysis

Cardio-rs supports window analysis, which allows you to split data into segments of a defined size. This is useful for performing HRV analysis on specific time periods or segments of data.

Example:

use cardio_rs::{ windows_analysis::WindowsAnalysisBuilder };

let data = vec![800., 810., 790., 765., 780., 800., 810., 795., 770., 785.];

// Create the window analysis builder
let analysis = WindowsAnalysisBuilder::new(data)
    .with_window_size(3_000.)
    .build();

// Get HRV metrics for each window
println!("{:?}", analysis.metrics);

🔄 Live Analysis

The live analysis module allows for real-time HRV calculations by dynamically processing incoming RR intervals. You can define a custom analysis pipeline or use the default pipeline for standard HRV metrics.

Example:

use cardio_rs::{ live_analysis::TimeQueue, HrvMetrics };
use cardio_rs::utils::test_data::RR_INTERVALS;

let mut queue = TimeQueue::new(60_000);  // Set the time window (in milliseconds)
let rr_intervals = RR_INTERVALS.to_vec();

for interval in rr_intervals {
    queue.push(interval);
}

let hrv = queue.get_hrv();
println!("Calculated HRV: {:?}", hrv);

🦀 Example Usage

🚀 Compute HRV Metrics & Load ECG Data

use cardio_rs::{ standard_analysis, HrvMetrics };

let path = "tests/ecg.csv";
let signal = "ECG_Raw";
let rate = 1000.;

let hrv_metrics = standard_analysis!(path, signal, rate);
println!("{:?}", hrv_metrics);

#[cfg(feature = "std")] {
use cardio_rs::{
    processing_utils::{ RRIntervals, EctopicMethod, DetectOutliers },
    time_domain::TimeMetrics,
    non_linear::NonLinearMetrics,
    geometric_domain::GeometricMetrics,
    frequency_domain::FrequencyMetrics,
    io_utils::{ DataBuilder },
    standard_analysis,
    HrvMetrics,
};

let path = "tests/ecg.csv";
let signal = "ECG_Raw";
let time = "Time";
let data = DataBuilder::new(path.into(), signal.into())
    .with_time(time.into())
    .build()
    .unwrap();

let hrv_metrics = standard_analysis!(data.clone().get_rr());
println!("{:?}", hrv_metrics);

// Manual analysis for more control
let mut rr_intervals = RRIntervals::new(data.get_rr());

rr_intervals.detect_ectopics(EctopicMethod::Karlsson);
rr_intervals.detect_outliers(&300., &2_000.);
rr_intervals.remove_outliers_ectopics();

let time_metrics = TimeMetrics::compute(rr_intervals.as_slice());
println!("{:?}", time_metrics);

let frequency_metrics = FrequencyMetrics::compute(rr_intervals.as_slice(), 10.);
println!("{:?}", frequency_metrics);

let geo_metrics = GeometricMetrics::compute(rr_intervals.as_slice());
println!("{:?}", geo_metrics);

let non_linear_metrics = NonLinearMetrics::compute_default(&rr_intervals);
println!("{:?}", non_linear_metrics);
}

🔥 Start analyzing HRV today with cardio-rs! 🚀💓