Clinical Workflows

from neurascale.clinical.rehabilitation import MotorImageryTherapy
 
# Configure stroke rehabilitation protocol
stroke_config = {
    "patient_profile": {
        "stroke_type": "ischemic",  # ischemic, hemorrhagic
        "affected_hemisphere": "left",
        "time_since_stroke": 90,  # days
        "motor_impairment": {
            "upper_extremity": "moderate",
            "hand_function": "severe",
            "fugl_meyer_score": 35  # out of 66
        }
    },
    "therapy_protocol": {
        "sessions_per_week": 3,
        "session_duration": 45,  # minutes
        "total_weeks": 12,
        "progression_criteria": {
            "accuracy_threshold": 0.70,
            "consistency_required": 5  # consecutive sessions
        }
    },
    "neurofeedback": {
        "feedback_type": "visual_cursor",
        "update_rate": 10,  # Hz
        "trial_duration": 4,  # seconds
        "rest_duration": 3   # seconds
    }
}
 
# Initialize motor imagery therapy
mi_therapy = MotorImageryTherapy(stroke_config)
 
async def conduct_therapy_session():
    """Conduct a motor imagery therapy session"""
 
    # Pre-session assessment
    baseline = await assess_motor_function(patient)
 
    # Configure BCI for motor imagery
    mi_bci_config = {
        "channels": ["C3", "C4", "Cz", "FC1", "FC2", "CP1", "CP2"],
        "frequency_bands": {
            "mu": [8, 12],
            "beta": [16, 24]
        },
        "classification": {
            "classes": ["left_hand", "right_hand", "rest"],
            "feature_extraction": "csp",  # Common Spatial Patterns
            "classifier": "lda"
        }
    }
 
    # Start therapy session
    session = await mi_therapy.start_session(
        patient_id=patient.anonymous_id,
        config=mi_bci_config
    )
 
    # Calibration phase
    print("Starting calibration...")
    calibration_data = await mi_therapy.run_calibration(
        trials_per_class=20,
        instruction_duration=2,
        imagery_duration=4
    )
 
    # Train classifier
    classifier = await mi_therapy.train_classifier(calibration_data)
    accuracy = await mi_therapy.validate_classifier(classifier)
 
    print(f"Classifier accuracy: {accuracy:.2%}")
 
    if accuracy < 0.60:
        print("Accuracy too low, adjusting parameters...")
        # Adjust parameters and retrain
        classifier = await mi_therapy.retrain_with_adjustments(
            calibration_data,
            adjustments={"feature_selection": True, "regularization": 0.1}
        )
 
    # Online therapy phase
    print("Starting online therapy...")
    therapy_results = await mi_therapy.run_online_therapy(
        classifier=classifier,
        trials=60,
        feedback_enabled=True
    )
 
    # Post-session assessment
    post_assessment = await assess_motor_function(patient)
 
    # Log session results
    session_summary = {
        "date": datetime.now().isoformat(),
        "duration": therapy_results["duration"],
        "accuracy": therapy_results["accuracy"],
        "improvement": post_assessment["score"] - baseline["score"],
        "patient_feedback": therapy_results["patient_feedback"]
    }
 
    await clinical_client.sessions.log_therapy_session(
        patient_id=patient.anonymous_id,
        session_type="motor_imagery",
        results=session_summary
    )
 
async def assess_motor_function(patient):
    """Assess motor function using standardized scales"""
 
    # Fugl-Meyer Assessment (automated components)
    fm_assessment = await conduct_fugl_meyer_assessment(patient)
 
    # Action Research Arm Test (ARAT)
    arat_score = await conduct_arat_assessment(patient)
 
    # Nine-Hole Peg Test
    peg_test = await conduct_peg_test(patient)
 
    assessment = {
        "fugl_meyer": fm_assessment,
        "arat": arat_score,
        "nine_hole_peg": peg_test,
        "overall_score": calculate_composite_score([
            fm_assessment, arat_score, peg_test
        ])
    }
 
    return assessment

async def track_rehabilitation_progress():
    """Track long-term rehabilitation progress"""
 
    # Get all therapy sessions for patient
    sessions = await clinical_client.sessions.get_patient_sessions(
        patient_id=patient.anonymous_id,
        session_type="motor_imagery"
    )
 
    # Analyze progress trends
    progress_analysis = {
        "accuracy_trend": analyze_accuracy_trend(sessions),
        "motor_improvement": analyze_motor_improvement(sessions),
        "session_consistency": analyze_session_consistency(sessions),
        "plateau_detection": detect_learning_plateau(sessions)
    }
 
    # Generate progress report
    progress_report = await generate_progress_report(progress_analysis)
 
    # Adjust therapy parameters if needed
    if progress_analysis["plateau_detection"]["plateau_detected"]:
        new_parameters = await suggest_parameter_adjustments(sessions)
        await mi_therapy.update_parameters(new_parameters)
 
    return progress_report
 
def analyze_accuracy_trend(sessions):
    """Analyze BCI accuracy improvement over time"""
 
    accuracies = [session["accuracy"] for session in sessions]
    dates = [session["date"] for session in sessions]
 
    # Linear regression to find trend
    from scipy import stats
    slope, intercept, r_value, p_value, std_err = stats.linregress(
        range(len(accuracies)), accuracies
    )
 
    trend_analysis = {
        "initial_accuracy": accuracies[0] if accuracies else 0,
        "current_accuracy": accuracies[-1] if accuracies else 0,
        "improvement_rate": slope,
        "correlation": r_value,
        "significance": p_value < 0.05,
        "total_improvement": accuracies[-1] - accuracies[0] if len(accuracies) > 1 else 0
    }
 
    return trend_analysis

from neurascale.clinical.sleep import SleepStudyProtocol
 
# Configure sleep study
sleep_config = {
    "study_type": "diagnostic",  # diagnostic, titration, split_night
    "channels": {
        "eeg": ["F4-M1", "C4-M1", "O2-M1"],  # Standard sleep EEG
        "eog": ["ROC-M1", "LOC-M1"],         # Eye movements
        "emg": ["chin", "left_leg", "right_leg"],  # Muscle activity
        "respiratory": ["airflow", "chest", "abdomen"],
        "cardiac": ["ECG"],
        "oxygen": ["SpO2"]
    },
    "staging": {
        "epoch_duration": 30,  # seconds
        "algorithm": "aasm_2017",  # AASM 2017 criteria
        "automatic_staging": True,
        "manual_review": True
    }
}
 
sleep_protocol = SleepStudyProtocol(sleep_config)
 
async def conduct_sleep_study():
    """Conduct overnight sleep study"""
 
    # Start sleep monitoring
    study_session = await sleep_protocol.start_study(
        patient_id=patient.anonymous_id,
        study_date=datetime.now()
    )
 
    # Monitor in real-time
    async def monitor_sleep():
        stream = await study_session.start_monitoring()
 
        async for epoch in stream:
            # 30-second epochs
            sleep_stage = await sleep_protocol.classify_epoch(epoch)
 
            # Detect sleep events
            events = await sleep_protocol.detect_events(epoch)
 
            # Log stage and events
            await study_session.log_epoch(
                stage=sleep_stage,
                events=events,
                quality=epoch.signal_quality
            )
 
            # Real-time alerts for significant events
            if events:
                for event in events:
                    if event.type == "apnea" and event.duration > 30:
                        await send_clinical_alert(
                            "prolonged_apnea",
                            patient.anonymous_id,
                            {"duration": event.duration}
                        )
 
    # Run overnight monitoring
    await monitor_sleep()
 
    # Generate sleep report
    sleep_report = await generate_sleep_report(study_session)
 
    return sleep_report
 
async def analyze_sleep_architecture(study_session):
    """Analyze sleep architecture and generate clinical metrics"""
 
    epochs = await study_session.get_all_epochs()
 
    # Calculate sleep metrics
    sleep_metrics = {
        "total_recording_time": len(epochs) * 0.5,  # minutes
        "total_sleep_time": sum(1 for e in epochs if e.stage != "wake") * 0.5,
        "sleep_efficiency": calculate_sleep_efficiency(epochs),
        "sleep_onset_latency": calculate_sleep_onset_latency(epochs),
        "rem_onset_latency": calculate_rem_onset_latency(epochs),
        "stage_percentages": calculate_stage_percentages(epochs),
        "arousal_index": calculate_arousal_index(epochs),
        "ahi": calculate_apnea_hypopnea_index(epochs)  # Apnea-Hypopnea Index
    }
 
    return sleep_metrics

from neurascale.clinical.validation import ClinicalDataValidator
 
class ClinicalQualityAssurance:
    def __init__(self):
        self.validator = ClinicalDataValidator()
        self.quality_thresholds = {
            "signal_quality": 0.85,
            "impedance_max": 5000,  # 5kΩ
            "artifact_percentage_max": 0.15,
            "data_completeness_min": 0.95
        }
 
    async def validate_clinical_session(self, session_id):
        """Comprehensive validation of clinical session data"""
 
        validation_results = {
            "session_id": session_id,
            "validation_timestamp": datetime.now().isoformat(),
            "tests_performed": [],
            "passed": True,
            "issues": []
        }
 
        # 1. Signal Quality Assessment
        signal_quality = await self.validator.assess_signal_quality(session_id)
        validation_results["tests_performed"].append("signal_quality")
 
        if signal_quality["average_quality"] < self.quality_thresholds["signal_quality"]:
            validation_results["passed"] = False
            validation_results["issues"].append({
                "type": "signal_quality",
                "severity": "warning",
                "message": f"Signal quality {signal_quality['average_quality']:.2f} below threshold"
            })
 
        # 2. Impedance Check
        impedance_data = await self.validator.check_impedances(session_id)
        validation_results["tests_performed"].append("impedance_check")
 
        high_impedance_channels = [
            ch for ch, imp in impedance_data.items()
            if imp > self.quality_thresholds["impedance_max"]
        ]
 
        if high_impedance_channels:
            validation_results["passed"] = False
            validation_results["issues"].append({
                "type": "high_impedance",
                "severity": "error",
                "message": f"High impedance on channels: {high_impedance_channels}"
            })
 
        # 3. Artifact Assessment
        artifact_analysis = await self.validator.analyze_artifacts(session_id)
        validation_results["tests_performed"].append("artifact_analysis")
 
        if artifact_analysis["percentage"] > self.quality_thresholds["artifact_percentage_max"]:
            validation_results["issues"].append({
                "type": "excessive_artifacts",
                "severity": "warning",
                "message": f"Artifacts present in {artifact_analysis['percentage']:.1%} of data"
            })
 
        # 4. Data Completeness
        completeness = await self.validator.check_data_completeness(session_id)
        validation_results["tests_performed"].append("data_completeness")
 
        if completeness < self.quality_thresholds["data_completeness_min"]:
            validation_results["passed"] = False
            validation_results["issues"].append({
                "type": "incomplete_data",
                "severity": "error",
                "message": f"Data completeness {completeness:.1%} below minimum threshold"
            })
 
        # 5. Clinical Protocol Compliance
        protocol_compliance = await self.validator.check_protocol_compliance(session_id)
        validation_results["tests_performed"].append("protocol_compliance")
 
        if not protocol_compliance["compliant"]:
            validation_results["issues"].extend([
                {
                    "type": "protocol_violation",
                    "severity": "error",
                    "message": violation
                }
                for violation in protocol_compliance["violations"]
            ])
 
        # Log validation results
        await clinical_client.audit.log_validation(validation_results)
 
        return validation_results
 
    async def generate_quality_report(self, validation_results):
        """Generate clinical quality assurance report"""
 
        report = {
            "validation_summary": validation_results,
            "quality_score": self.calculate_quality_score(validation_results),
            "recommendations": self.generate_recommendations(validation_results),
            "clinical_usability": self.assess_clinical_usability(validation_results)
        }
 
        return report
 
# Clinical audit trail
async def maintain_audit_trail():
    """Maintain comprehensive audit trail for clinical compliance"""
 
    audit_events = [
        "patient_access",
        "data_modification",
        "report_generation",
        "data_export",
        "system_login",
        "configuration_change"
    ]
 
    for event_type in audit_events:
        await clinical_client.audit.configure_logging(
            event_type=event_type,
            log_level="detailed",
            retention_period=2555,  # 7 years
            encryption=True
        )

# Generate FDA-compliant documentation
async def generate_fda_documentation():
    """Generate documentation for FDA submission"""
 
    fda_docs = {
        "device_description": await generate_device_description(),
        "clinical_protocol": await generate_clinical_protocol(),
        "safety_analysis": await generate_safety_analysis(),
        "efficacy_data": await generate_efficacy_data(),
        "quality_system": await generate_quality_system_docs(),
        "risk_analysis": await generate_risk_analysis()
    }
 
    # Compile comprehensive submission package
    submission_package = await compile_fda_submission(fda_docs)
 
    return submission_package
 
async def generate_clinical_protocol():
    """Generate clinical protocol documentation"""
 
    protocol = {
        "title": "Clinical Evaluation of NeuraScale BCI System",
        "objectives": {
            "primary": "Demonstrate safety and efficacy for intended use",
            "secondary": ["User satisfaction", "System reliability", "Clinical workflow integration"]
        },
        "study_design": {
            "type": "prospective_clinical_study",
            "duration": "12_months",
            "sample_size": 50,
            "inclusion_criteria": [...],
            "exclusion_criteria": [...]
        },
        "endpoints": {
            "primary": "Classification accuracy > 70%",
            "secondary": ["System uptime > 99%", "User satisfaction > 4/5"]
        },
        "statistical_plan": {
            "power_analysis": "80% power to detect 10% difference",
            "alpha_level": 0.05,
            "analysis_method": "intention_to_treat"
        }
    }
 
    return protocol

from neurascale.clinical.integration import HL7FHIRInterface
 
# Configure hospital system integration
hospital_integration = {
    "ehr_system": "epic",  # epic, cerner, allscripts
    "fhir_server": "https://fhir.hospital.org/R4",
    "authentication": {
        "method": "oauth2",
        "client_id": "neurascale_client",
        "scopes": ["patient/read", "observation/write", "diagnosticreport/write"]
    }
}
 
fhir_client = HL7FHIRInterface(hospital_integration)
 
async def sync_with_ehr():
    """Synchronize NeuraScale data with hospital EHR"""
 
    # Get patient data from EHR
    patient_fhir = await fhir_client.get_patient(patient.hospital_id)
 
    # Create FHIR observation for EEG data
    eeg_observation = {
        "resourceType": "Observation",
        "status": "final",
        "category": [
            {
                "coding": [
                    {
                        "system": "http://terminology.hl7.org/CodeSystem/observation-category",
                        "code": "survey"
                    }
                ]
            }
        ],
        "code": {
            "coding": [
                {
                    "system": "http://loinc.org",
                    "code": "54649-5",
                    "display": "EEG study"
                }
            ]
        },
        "subject": {"reference": f"Patient/{patient.hospital_id}"},
        "effectiveDateTime": session.start_time.isoformat(),
        "valueString": "Normal EEG with no epileptiform activity"
    }
 
    # Submit to EHR
    await fhir_client.create_observation(eeg_observation)