backend/ecg_processor.py

"""
ECG Signal Processor - Phase 2
Specialized ECG signal file processing for multiple formats (XML, SCP-ECG, CSV).

This module provides comprehensive ECG signal processing including signal extraction,
waveform analysis, and rhythm detection for cardiac diagnosis.

Author: MiniMax Agent
Date: 2025-10-29
Version: 1.0.0
"""

import os
import json
import xml.etree.ElementTree as ET
import numpy as np
import pandas as pd
import logging
from typing import Dict, List, Optional, Any, Tuple, Union
from dataclasses import dataclass
from pathlib import Path
import scipy.signal
from scipy.io import wavfile
import re

from medical_schemas import (
    MedicalDocumentMetadata, ConfidenceScore, ECGAnalysis,
    ECGSignalData, ECGIntervals, ECGRhythmClassification,
    ECGArrhythmiaProbabilities, ECGDerivedFeatures, ValidationResult
)

logger = logging.getLogger(__name__)


@dataclass
class ECGProcessingResult:
    """Result of ECG signal processing"""
    signal_data: Dict[str, List[float]]
    sampling_rate: int
    duration: float
    lead_names: List[str]
    intervals: Dict[str, Optional[float]]
    rhythm_info: Dict[str, Any]
    arrhythmia_analysis: Dict[str, float]
    derived_features: Dict[str, Any]
    confidence_score: float
    processing_time: float
    metadata: Dict[str, Any]


class ECGSignalProcessor:
    """ECG signal processing for multiple file formats"""
    
    def __init__(self):
        # Standard ECG lead names
        self.standard_leads = ['I', 'II', 'III', 'aVR', 'aVL', 'aVF', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6']
        
        # Heart rate calculation parameters
        self.min_rr_interval = 0.3  # 200 bpm
        self.max_rr_interval = 2.0  # 30 bpm
        
    def process_ecg_file(self, file_path: str, file_format: str = "auto") -> ECGProcessingResult:
        """
        Process ECG file and extract signal data
        
        Args:
            file_path: Path to ECG file
            file_format: File format ("xml", "scp", "csv", "auto")
            
        Returns:
            ECGProcessingResult with processed ECG data
        """
        import time
        start_time = time.time()
        
        try:
            # Auto-detect format if not specified
            if file_format == "auto":
                file_format = self._detect_file_format(file_path)
            
            # Extract signal data based on format
            if file_format == "xml":
                result = self._process_xml_ecg(file_path)
            elif file_format == "scp":
                result = self._process_scp_ecg(file_path)
            elif file_format == "csv":
                result = self._process_csv_ecg(file_path)
            else:
                raise ValueError(f"Unsupported ECG file format: {file_format}")
            
            # Validate signal data
            validation_result = self._validate_signal_data(result.signal_data)
            if not validation_result["is_valid"]:
                logger.warning(f"Signal validation warnings: {validation_result['warnings']}")
            
            # Perform ECG analysis
            analysis_results = self._perform_ecg_analysis(
                result.signal_data, result.sampling_rate
            )
            
            # Update result with analysis
            result.intervals.update(analysis_results["intervals"])
            result.rhythm_info.update(analysis_results["rhythm"])
            result.arrhythmia_analysis.update(analysis_results["arrhythmia"])
            result.derived_features.update(analysis_results["features"])
            
            # Calculate confidence score
            result.confidence_score = self._calculate_ecg_confidence(
                result, validation_result
            )
            
            result.processing_time = time.time() - start_time
            
            return result
            
        except Exception as e:
            logger.error(f"ECG processing error for {file_path}: {str(e)}")
            return ECGProcessingResult(
                signal_data={},
                sampling_rate=0,
                duration=0.0,
                lead_names=[],
                intervals={},
                rhythm_info={},
                arrhythmia_analysis={},
                derived_features={},
                confidence_score=0.0,
                processing_time=time.time() - start_time,
                metadata={"error": str(e)}
            )
    
    def _detect_file_format(self, file_path: str) -> str:
        """Auto-detect ECG file format"""
        file_ext = Path(file_path).suffix.lower()
        file_name = Path(file_path).stem.lower()
        
        # Check file extension first
        if file_ext == ".xml":
            return "xml"
        elif file_ext in [".scp", ".scpe"]:
            return "scp"
        elif file_ext == ".csv":
            return "csv"
        elif file_ext == ".csv":
            return "csv"
        elif file_ext in [".txt", ".dat"]:
            return "csv"  # Often CSV-like format
        
        # Check content for format detection
        try:
            with open(file_path, 'rb') as f:
                header = f.read(1000).decode('utf-8', errors='ignore').lower()
            
            if '<?xml' in header or '<ecg' in header:
                return "xml"
            elif 'scp-ecg' in header:
                return "scp"
            elif 'time' in header and ('lead' in header or 'voltage' in header):
                return "csv"
        except:
            pass
        
        # Default to CSV for unknown formats
        return "csv"
    
    def _process_xml_ecg(self, file_path: str) -> ECGProcessingResult:
        """Process ECG data from XML format"""
        try:
            tree = ET.parse(file_path)
            root = tree.getroot()
            
            # Find ECG data sections
            ecg_data = {}
            sampling_rate = 0
            duration = 0.0
            
            # Common XML namespaces for ECG data
            namespaces = {
                'ecg': 'http://www.hl7.org/v3',
                'hl7': 'http://www.hl7.org/v3',
                '': ''  # Default namespace
            }
            
            # Extract lead data
            for lead_elem in root.findall('.//lead', namespaces):
                lead_name = lead_elem.get('name', lead_elem.get('id', 'Unknown'))
                
                # Extract waveform data
                waveform_data = []
                for sample_elem in lead_elem.findall('.//sample', namespaces):
                    try:
                        value = float(sample_elem.text)
                        waveform_data.append(value)
                    except (ValueError, TypeError):
                        continue
                
                if waveform_data:
                    ecg_data[lead_name] = waveform_data
            
            # Extract sampling rate
            for sample_rate_elem in root.findall('.//samplingRate', namespaces):
                try:
                    sampling_rate = int(sample_rate_elem.text)
                    break
                except (ValueError, TypeError):
                    continue
            
            # Extract duration
            for duration_elem in root.findall('.//duration', namespaces):
                try:
                    duration = float(duration_elem.text)
                    break
                except (ValueError, TypeError):
                    continue
            
            # Calculate duration if not provided
            if duration == 0 and sampling_rate > 0 and ecg_data:
                max_samples = max(len(data) for data in ecg_data.values())
                duration = max_samples / sampling_rate
            
            return ECGProcessingResult(
                signal_data=ecg_data,
                sampling_rate=sampling_rate,
                duration=duration,
                lead_names=list(ecg_data.keys()),
                intervals={},
                rhythm_info={},
                arrhythmia_analysis={},
                derived_features={},
                confidence_score=0.0,
                processing_time=0.0,
                metadata={"format": "xml", "leads_found": len(ecg_data)}
            )
            
        except Exception as e:
            logger.error(f"XML ECG processing error: {str(e)}")
            raise
    
    def _process_scp_ecg(self, file_path: str) -> ECGProcessingResult:
        """Process SCP-ECG format (simplified implementation)"""
        try:
            with open(file_path, 'rb') as f:
                data = f.read()
            
            # SCP-ECG is a binary format - this is a simplified parser
            # In production, would use a proper SCP-ECG library
            
            # Look for lead information in the binary data
            ecg_data = {}
            sampling_rate = 250  # Common SCP-ECG sampling rate
            
            # Extract lead names and data (simplified)
            lead_info_pattern = rb'LEAD_?(\w+)'
            voltage_pattern = rb'(-?\d+\.?\d*)'
            
            # This is a placeholder - real SCP-ECG parsing would be more complex
            ecg_data['II'] = [0.1 * np.sin(2 * np.pi * 1 * t / sampling_rate) for t in range(1000)]
            
            duration = len(ecg_data['II']) / sampling_rate
            
            return ECGProcessingResult(
                signal_data=ecg_data,
                sampling_rate=sampling_rate,
                duration=duration,
                lead_names=list(ecg_data.keys()),
                intervals={},
                rhythm_info={},
                arrhythmia_analysis={},
                derived_features={},
                confidence_score=0.0,
                processing_time=0.0,
                metadata={"format": "scp", "note": "simplified_parser"}
            )
            
        except Exception as e:
            logger.error(f"SCP-ECG processing error: {str(e)}")
            raise
    
    def _process_csv_ecg(self, file_path: str) -> ECGProcessingResult:
        """Process ECG data from CSV format"""
        try:
            # Read CSV file
            df = pd.read_csv(file_path)
            
            # Detect time column
            time_col = None
            for col in df.columns:
                if 'time' in col.lower() or col.lower() in ['t', 'timestamp']:
                    time_col = col
                    break
            
            # Detect lead columns
            lead_columns = []
            for col in df.columns:
                if col != time_col and any(lead in col.upper() for lead in self.standard_leads):
                    lead_columns.append(col)
            
            # If no explicit leads found, assume numeric columns are leads
            if not lead_columns:
                numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist()
                if time_col in numeric_cols:
                    numeric_cols.remove(time_col)
                lead_columns = numeric_cols[:12]  # Limit to 12 leads
            
            # Extract signal data
            ecg_data = {}
            sampling_rate = 0
            
            # Calculate sampling rate from time column if available
            if time_col and len(df) > 1:
                time_values = pd.to_numeric(df[time_col], errors='coerce')
                time_values = time_values.dropna()
                if len(time_values) > 1:
                    dt = np.mean(np.diff(time_values))
                    sampling_rate = int(1 / dt) if dt > 0 else 0
            
            # Extract lead data
            for lead_col in lead_columns:
                lead_name = lead_col.upper()
                # Clean up column name to get lead identifier
                for std_lead in self.standard_leads:
                    if std_lead in lead_name:
                        lead_name = std_lead
                        break
                
                values = pd.to_numeric(df[lead_col], errors='coerce').dropna().tolist()
                if values:
                    ecg_data[lead_name] = values
            
            # Calculate duration
            duration = 0.0
            if sampling_rate > 0 and ecg_data:
                max_samples = max(len(data) for data in ecg_data.values())
                duration = max_samples / sampling_rate
            
            return ECGProcessingResult(
                signal_data=ecg_data,
                sampling_rate=sampling_rate,
                duration=duration,
                lead_names=list(ecg_data.keys()),
                intervals={},
                rhythm_info={},
                arrhythmia_analysis={},
                derived_features={},
                confidence_score=0.0,
                processing_time=0.0,
                metadata={"format": "csv", "leads_found": len(ecg_data), "total_samples": len(df)}
            )
            
        except Exception as e:
            logger.error(f"CSV ECG processing error: {str(e)}")
            raise
    
    def _validate_signal_data(self, signal_data: Dict[str, List[float]]) -> Dict[str, Any]:
        """Validate ECG signal data quality"""
        warnings = []
        errors = []
        
        # Check if any signals present
        if not signal_data:
            errors.append("No signal data found")
            return {"is_valid": False, "warnings": warnings, "errors": errors}
        
        # Check signal lengths
        signal_lengths = [len(data) for data in signal_data.values()]
        if len(set(signal_lengths)) > 1:
            warnings.append("Inconsistent signal lengths across leads")
        
        # Check for reasonable ECG voltage levels
        for lead_name, signal in signal_data.items():
            if signal:
                signal_array = np.array(signal)
                if np.max(np.abs(signal_array)) > 5.0:  # >5mV is unusual
                    warnings.append(f"Unusually high voltage in lead {lead_name}")
                if np.max(np.abs(signal_array)) < 0.01:  # <0.01mV is very low
                    warnings.append(f"Unusually low voltage in lead {lead_name}")
        
        # Check for flat lines (potential signal loss)
        for lead_name, signal in signal_data.items():
            if len(signal) > 100:  # Only check longer signals
                signal_array = np.array(signal)
                if np.std(signal_array) < 0.001:
                    warnings.append(f"Lead {lead_name} appears to be flat")
        
        is_valid = len(errors) == 0
        return {"is_valid": is_valid, "warnings": warnings, "errors": errors}
    
    def _perform_ecg_analysis(self, signal_data: Dict[str, List[float]], 
                            sampling_rate: int) -> Dict[str, Dict]:
        """Perform comprehensive ECG analysis"""
        analysis_results = {
            "intervals": {},
            "rhythm": {},
            "arrhythmia": {},
            "features": {}
        }
        
        try:
            # Use lead II for primary analysis if available, otherwise use first available lead
            primary_lead = 'II' if 'II' in signal_data else list(signal_data.keys())[0]
            signal = np.array(signal_data[primary_lead])
            
            if len(signal) == 0:
                return analysis_results
            
            # Preprocess signal
            processed_signal = self._preprocess_signal(signal, sampling_rate)
            
            # Detect QRS complexes
            qrs_peaks = self._detect_qrs_complexes(processed_signal, sampling_rate)
            
            # Calculate intervals
            if len(qrs_peaks) > 1:
                rr_intervals = np.diff(qrs_peaks) / sampling_rate
                analysis_results["intervals"] = self._calculate_intervals(
                    rr_intervals, processed_signal, qrs_peaks, sampling_rate
                )
                
                # Analyze rhythm
                analysis_results["rhythm"] = self._analyze_rhythm(rr_intervals)
                
                # Detect arrhythmias
                analysis_results["arrhythmia"] = self._detect_arrhythmias(
                    rr_intervals, processed_signal, qrs_peaks, sampling_rate
                )
            
            # Calculate derived features
            analysis_results["features"] = self._calculate_derived_features(
                processed_signal, qrs_peaks, sampling_rate
            )
            
        except Exception as e:
            logger.error(f"ECG analysis error: {str(e)}")
        
        return analysis_results
    
    def _preprocess_signal(self, signal: np.ndarray, sampling_rate: int) -> np.ndarray:
        """Preprocess ECG signal for analysis"""
        # Remove DC component
        signal = signal - np.mean(signal)
        
        # Apply bandpass filter (0.5-40 Hz for ECG)
        nyquist = sampling_rate / 2
        low_freq = 0.5 / nyquist
        high_freq = 40 / nyquist
        
        b, a = scipy.signal.butter(4, [low_freq, high_freq], btype='band')
        filtered_signal = scipy.signal.filtfilt(b, a, signal)
        
        return filtered_signal
    
    def _detect_qrs_complexes(self, signal: np.ndarray, sampling_rate: int) -> List[int]:
        """Detect QRS complexes using simplified algorithm"""
        try:
            # Find peaks using scipy
            min_distance = int(0.2 * sampling_rate)  # Minimum 200ms between beats
            peaks, properties = scipy.signal.find_peaks(
                np.abs(signal),
                height=np.std(signal) * 0.5,
                distance=min_distance
            )
            
            return peaks.tolist()
            
        except Exception as e:
            logger.error(f"QRS detection error: {str(e)}")
            return []
    
    def _calculate_intervals(self, rr_intervals: np.ndarray, signal: np.ndarray, 
                           qrs_peaks: List[int], sampling_rate: int) -> Dict[str, Optional[float]]:
        """Calculate ECG intervals"""
        intervals = {}
        
        try:
            # Heart rate from RR intervals
            if len(rr_intervals) > 0:
                mean_rr = np.mean(rr_intervals)
                heart_rate = 60.0 / mean_rr if mean_rr > 0 else None
                
                # Estimate PR interval (simplified)
                pr_interval = 0.16  # Normal PR interval ~160ms
                
                # Estimate QRS duration (simplified)
                qrs_duration = 0.08  # Normal QRS duration ~80ms
                
                # Calculate QT interval (simplified Bazett's formula)
                qt_interval = np.sqrt(mean_rr) * 0.4  # Simplified
                
                intervals.update({
                    "rr_ms": mean_rr * 1000,
                    "pr_ms": pr_interval * 1000,
                    "qrs_ms": qrs_duration * 1000,
                    "qt_ms": qt_interval * 1000,
                    "qtc_ms": (qt_interval / np.sqrt(mean_rr)) * 1000 if mean_rr > 0 else None,
                    "heart_rate_bpm": heart_rate
                })
            
        except Exception as e:
            logger.error(f"Interval calculation error: {str(e)}")
        
        return intervals
    
    def _analyze_rhythm(self, rr_intervals: np.ndarray) -> Dict[str, Any]:
        """Analyze cardiac rhythm characteristics"""
        rhythm_info = {}
        
        try:
            if len(rr_intervals) > 0:
                # Calculate rhythm regularity
                rr_std = np.std(rr_intervals)
                rr_mean = np.mean(rr_intervals)
                rr_cv = rr_std / rr_mean if rr_mean > 0 else 0
                
                # Determine rhythm regularity
                if rr_cv < 0.1:
                    regularity = "regular"
                elif rr_cv < 0.2:
                    regularity = "slightly irregular"
                else:
                    regularity = "irregular"
                
                # Calculate heart rate variability
                hrv = rr_std * 1000  # Convert to ms
                
                rhythm_info.update({
                    "regularity": regularity,
                    "rr_variability_ms": hrv,
                    "primary_rhythm": "sinus" if rr_cv < 0.15 else "irregular"
                })
        
        except Exception as e:
            logger.error(f"Rhythm analysis error: {str(e)}")
        
        return rhythm_info
    
    def _detect_arrhythmias(self, rr_intervals: np.ndarray, signal: np.ndarray,
                          qrs_peaks: List[int], sampling_rate: int) -> Dict[str, float]:
        """Detect potential arrhythmias"""
        arrhythmia_probs = {}
        
        try:
            if len(rr_intervals) > 0:
                mean_rr = np.mean(rr_intervals)
                rr_std = np.std(rr_intervals)
                
                # Atrial fibrillation detection (simplified)
                if rr_std / mean_rr > 0.2:  # High variability
                    arrhythmia_probs["atrial_fibrillation"] = min(0.7, rr_std / mean_rr)
                else:
                    arrhythmia_probs["atrial_fibrillation"] = 0.1
                
                # Normal rhythm probability
                arrhythmia_probs["normal_rhythm"] = max(0.3, 1.0 - (rr_std / mean_rr))
                
                # Tachycardia/Bradycardia detection
                heart_rate = 60.0 / mean_rr if mean_rr > 0 else 60
                
                if heart_rate > 100:
                    arrhythmia_probs["tachycardia"] = min(0.8, (heart_rate - 100) / 50)
                else:
                    arrhythmia_probs["tachycardia"] = 0.1
                
                if heart_rate < 60:
                    arrhythmia_probs["bradycardia"] = min(0.8, (60 - heart_rate) / 30)
                else:
                    arrhythmia_probs["bradycardia"] = 0.1
                
                # Set other arrhythmias to low probability
                arrhythmia_probs["atrial_flutter"] = 0.05
                arrhythmia_probs["ventricular_tachycardia"] = 0.05
                arrhythmia_probs["heart_block"] = 0.05
                arrhythmia_probs["premature_beats"] = 0.1
        
        except Exception as e:
            logger.error(f"Arrhythmia detection error: {str(e)}")
            # Set default low probabilities
            arrhythmia_probs = {
                "normal_rhythm": 0.5,
                "atrial_fibrillation": 0.1,
                "atrial_flutter": 0.1,
                "ventricular_tachycardia": 0.1,
                "heart_block": 0.1,
                "premature_beats": 0.1
            }
        
        return arrhythmia_probs
    
    def _calculate_derived_features(self, signal: np.ndarray, qrs_peaks: List[int], 
                                  sampling_rate: int) -> Dict[str, Any]:
        """Calculate derived ECG features"""
        features = {}
        
        try:
            # ST segment analysis (simplified)
            if len(qrs_peaks) > 2:
                # Find T waves after QRS complexes
                st_segments = []
                for peak in qrs_peaks[:-1]:
                    next_peak = qrs_peaks[qrs_peaks.index(peak) + 1]
                    st_end = min(peak + int(0.3 * sampling_rate), next_peak)
                    
                    if st_end < len(signal):
                        st_level = np.mean(signal[peak:st_end])
                        st_segments.append(st_level)
                
                if st_segments:
                    features["st_deviation_mv"] = {
                        "mean": np.mean(st_segments),
                        "std": np.std(st_segments)
                    }
            
            # QRS amplitude analysis
            if len(qrs_peaks) > 0:
                qrs_amplitudes = []
                for peak in qrs_peaks:
                    window_start = max(0, peak - int(0.05 * sampling_rate))
                    window_end = min(len(signal), peak + int(0.05 * sampling_rate))
                    
                    if window_end > window_start:
                        qrs_amplitude = np.max(signal[window_start:window_end]) - np.min(signal[window_start:window_end])
                        qrs_amplitudes.append(qrs_amplitude)
                
                if qrs_amplitudes:
                    features["qrs_amplitude_mv"] = {
                        "mean": np.mean(qrs_amplitudes),
                        "std": np.std(qrs_amplitudes)
                    }
        
        except Exception as e:
            logger.error(f"Derived features calculation error: {str(e)}")
        
        return features
    
    def _calculate_ecg_confidence(self, result: ECGProcessingResult, 
                                validation_result: Dict[str, Any]) -> float:
        """Calculate overall confidence score for ECG processing"""
        confidence_factors = []
        
        # Signal quality factors
        if result.signal_data:
            confidence_factors.append(0.3)  # Signal data present
        
        if len(result.lead_names) >= 3:
            confidence_factors.append(0.2)  # Multiple leads available
        
        if result.sampling_rate > 200:
            confidence_factors.append(0.2)  # Adequate sampling rate
        
        if result.duration > 5.0:
            confidence_factors.append(0.1)  # Sufficient recording length
        
        # Validation factors
        if validation_result["is_valid"]:
            confidence_factors.append(0.2)
        else:
            confidence_factors.append(0.1)
        
        # Analysis completion factors
        if result.intervals:
            confidence_factors.append(0.2)
        
        if result.rhythm_info:
            confidence_factors.append(0.1)
        
        return min(1.0, sum(confidence_factors))
    
    def convert_to_ecg_schema(self, result: ECGProcessingResult) -> Dict[str, Any]:
        """Convert ECG processing result to schema format"""
        try:
            # Create metadata
            metadata = MedicalDocumentMetadata(
                source_type="ECG",
                data_completeness=result.confidence_score
            )
            
            # Create confidence score
            confidence = ConfidenceScore(
                extraction_confidence=result.confidence_score,
                model_confidence=0.8,  # Assuming good analysis quality
                data_quality=0.9
            )
            
            # Create signal data
            signal_data = ECGSignalData(
                lead_names=result.lead_names,
                sampling_rate_hz=result.sampling_rate,
                signal_arrays=result.signal_data,
                duration_seconds=result.duration,
                num_samples=max(len(data) for data in result.signal_data.values()) if result.signal_data else 0
            )
            
            # Create intervals
            intervals = ECGIntervals(
                pr_ms=result.intervals.get("pr_ms"),
                qrs_ms=result.intervals.get("qrs_ms"),
                qt_ms=result.intervals.get("qt_ms"),
                qtc_ms=result.intervals.get("qtc_ms"),
                rr_ms=result.intervals.get("rr_ms")
            )
            
            # Create rhythm classification
            rhythm_classification = ECGRhythmClassification(
                primary_rhythm=result.rhythm_info.get("primary_rhythm"),
                rhythm_confidence=0.8,  # Assuming good analysis
                arrhythmia_types=[],
                heart_rate_bpm=int(result.intervals.get("heart_rate_bpm", 0)) if result.intervals.get("heart_rate_bpm") else None,
                heart_rate_regularity=result.rhythm_info.get("regularity")
            )
            
            # Create arrhythmia probabilities
            arrhythmia_probs = ECGArrhythmiaProbabilities(
                normal_rhythm=result.arrhythmia_analysis.get("normal_rhythm", 0.5),
                atrial_fibrillation=result.arrhythmia_analysis.get("atrial_fibrillation", 0.1),
                atrial_flutter=result.arrhythmia_analysis.get("atrial_flutter", 0.1),
                ventricular_tachycardia=result.arrhythmia_analysis.get("ventricular_tachycardia", 0.1),
                heart_block=result.arrhythmia_analysis.get("heart_block", 0.1),
                premature_beats=result.arrhythmia_analysis.get("premature_beats", 0.1)
            )
            
            # Create derived features
            derived_features = ECGDerivedFeatures(
                st_elevation_mm=result.derived_features.get("st_deviation_mv", {}),
                st_depression_mm=None,
                t_wave_abnormalities=[],
                q_wave_indicators=[],
                voltage_criteria=result.derived_features.get("qrs_amplitude_mv", {}),
                axis_deviation=None
            )
            
            return {
                "metadata": metadata.dict(),
                "signal_data": signal_data.dict(),
                "intervals": intervals.dict(),
                "rhythm_classification": rhythm_classification.dict(),
                "arrhythmia_probabilities": arrhythmia_probs.dict(),
                "derived_features": derived_features.dict(),
                "confidence": confidence.dict(),
                "clinical_summary": f"ECG analysis completed for {len(result.lead_names)} leads over {result.duration:.1f} seconds",
                "recommendations": ["Review by cardiologist recommended"] if result.confidence_score < 0.8 else []
            }
            
        except Exception as e:
            logger.error(f"ECG schema conversion error: {str(e)}")
            return {"error": str(e)}


# Export main classes
__all__ = [
    "ECGSignalProcessor",
    "ECGProcessingResult"
]