Each module starts from the saved ECG/RR study and adds a specific analytical view. Conventional signal analysis is kept separate from exploratory software-derived interpretations so the user can understand what is measured, what is estimated and what should be treated cautiously.
Real software viewsThe screenshots below come from example Kardio·AS studies. Numerical values belong to those recordings and are not universal reference values.
HRV
HRV Analysis
Beat-to-beat variability analysis built from accepted NN intervals, with time-domain, frequency-domain, nonlinear and graphical views.
What it analyses
RR/NN interval series and heart-rate variability
Time-domain variability metrics
Frequency-domain VLF, LF, HF and total power
Poincaré geometry and nonlinear characteristics
Signal acceptance and rhythm context
Typical outputs
Mean heart rate, SDNN, RMSSD and related HRV metrics
Spectral power distribution and LF/HF context
Poincaré plot and variability visualization
Stress-related and autonomic context used by downstream modules
HRV values are strongly affected by recording duration, posture, breathing, activity, sleep, substances, medication, illness and signal quality. Repeated studies are most useful when measurement conditions are comparable.
CV
Cardiovascular System
An integrated functional view that combines accepted HRV/rhythm metrics with local software-derived cardiovascular indices and age/sex display adjustments.
What it analyses
Heart rate, variability and regulatory load
Stress-resistance and adaptation-related patterns
Fatigue-related functional indices
Vascular stiffness/tone software indices
Integrated cardiovascular risk and health-oriented scores
How it is presented
Compact numerical summaries
Age/sex-adjusted display scales
Source-aware values linked back to accepted HRV analysis
Comparison and trend availability for repeated studies
These integrated indices are engineering/heuristic software outputs. They are not calibrated diagnostic biomarkers or stand-alone medical risk probabilities.
RR
Rhythm Pattern Analysis
A rhythm-focused review of the interval series, irregularity, recurring patterns and supporting variability characteristics.
What it analyses
Rhythmogram and beat-to-beat interval structure
Regularity and irregularity patterns
Premature/ectopic-like interval patterns
Entropy, DFA and supporting nonlinear context
Pattern frequency within the accepted recording
Why it is useful
Shows how the interval sequence behaves over time
Separates rhythm-pattern review from summary HRV numbers
Helps identify segments that deserve direct ECG review
Supports before/after comparison of rhythm dynamics
Pattern screening supports review of the recording; it does not replace a clinical rhythm diagnosis.
RSA
Respiratory Regulation
Indirect reconstruction of respiratory modulation from accepted NN/RR dynamics, including respiratory sinus arrhythmia and respiratory-band behavior.
What it estimates
Dominant respiratory rate
Respiratory-band dynamics
Respiratory sinus arrhythmia behavior
Breathing-rate variability within the recording
Signal confidence and supporting spectral context
How it is used
Resting respiratory-rate estimation without a separate breathing sensor
Cardiorespiratory context for HRV interpretation
Input to the exploratory emotional-state model
Longitudinal follow-up when studies are performed under similar conditions
Respiratory analysis is derived indirectly from RR/NN modulation. It is not spirometry, airflow measurement, oxygen saturation, capnography or a direct measurement of breathing.
ECG
ECG Analysis
Single-channel ECG review from the original trace through R-peak timing, QRS analysis and a representative median beat with P/Q/R/S/T morphology markers.
Signal and rhythm review
Signal quality and recording context
R-peak detection and ECG-derived RR intervals
QRS onset/offset and duration-related features
Rhythm, pauses and ectopic-like event review
Representative median beat construction
Morphology view
P, Q, R, S and T characteristic points
P/QRS/T region boundaries when available
Interval and amplitude relationships
Pattern/hypothesis evidence with confidence-aware presentation
Direct visual return to the waveform for expert review
Kardio·AS uses a single non-standard chest-sensor ECG channel. This is substantially different from a diagnostic 12-lead ECG, and software hypotheses must not be treated as a definitive diagnosis.
VA
Emotional State Profile
An exploratory autonomic interpretation that combines HRV and respiratory features into valence/arousal-style software coordinates and a dominant-state estimate.
What it uses
Accepted HRV metrics
Respiratory modulation features
Autonomic balance and variability context
Software-derived valence/arousal mapping
What it shows
Valence/arousal-style position
Dominant emotional-state estimate
Autonomic context supporting the estimate
Comparison with another saved study
This module does not directly measure emotion and does not provide a psychiatric or psychological diagnosis. It is an exploratory physiological interpretation.
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Advanced & Experimental Insights
Optional exploratory profiles derived from HRV patterns. They are deliberately separated from conventional HRV/ECG outputs.
Ayurvedic HRV Profile
Experimental wellness interpretation based on HRV frequency components and heart rate
Describes the current recording rather than a permanent constitutional type
Designed for exploratory comparison and observation
HRV-derived Neuroregulatory Profile
Conceptual Delta-like, Theta-like, Alpha-like, Beta-like and Gamma-like software categories
Derived from cardiovascular variability patterns
Intended for exploratory trend and comparison use
Kardio·AS does not record EEG and does not measure cerebral electrical activity or brain-wave frequencies. These experimental outputs are not validated clinical biomarkers.
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Result Comparison
Side-by-side review of two saved recordings from the same patient to make before/after or repeated-study change easier to interpret.
Comparison workflow
Select two studies from the same patient
Review matched HRV and cardiovascular values
Compare respiratory and exploratory outputs where available
Keep date, duration and study context visible
Best practice
Use similar body position
Use similar recording duration
Prefer similar time of day and resting conditions
Interpret change together with the original study quality
Repeated measurements under comparable conditions are generally more informative than isolated single values.
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Trends & Reports
Longitudinal follow-up that keeps each trend point connected to a real saved study instead of reducing history to an unexplained summary number.
Trend workflow
Patient-specific Trends & Calendar
Period selection for short- and longer-term review
Up to four selected parameters shown as separate graphs
Individual study points with date/time, duration and value
Open the source study from the trend context
Reporting
Structured study PDF reports
Trend PDF for the selected patient, period and graphs
Desktop and mobile analytical context
Local generation and user-controlled sharing/export
Trend interpretation is strongest when measurements are repeated under similar conditions and when signal quality remains acceptable across studies.
See the same study from several perspectives.
Return to the product overview, try the Android workflow or contact us about the Windows desktop version.
